Overview

Dataset statistics

Number of variables139
Number of observations522
Missing cells9891
Missing cells (%)13.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory567.0 KiB
Average record size in memory1.1 KiB

Variable types

Numeric27
Categorical37
DateTime2
Unsupported8
Text9
Boolean56

Alerts

phone_number has constant value "89000001"Constant
Renal complication has constant value "False"Constant
Type of renal complication has constant value "False"Constant
Bleeding post operation at the site of surgery that requires transfer of >= 4 packet cells within 72 hrs after surgery has constant value "False"Constant
Coma has constant value "False"Constant
Major wound disruption has constant value "False"Constant
Graft rejection has constant value "False"Constant
Sepsis has constant value "False"Constant
Systemic Inflammatory Response Syndrome (SISS) has constant value "0"Constant
Unplanned Intubation has constant value "False"Constant
mv_48h_or_more has constant value "False"Constant
death_post_discharge has constant value "False"Constant
AFib-tachycardia is highly overall correlated with Blood Loss during surgery and 3 other fieldsHigh correlation
Analgesics Post Operation is highly overall correlated with Antibiotics Post Operation and 5 other fieldsHigh correlation
Anesthesia type is highly overall correlated with Urine OutputHigh correlation
Antibiotics Post Operation is highly overall correlated with Analgesics Post Operation and 2 other fieldsHigh correlation
Anticoagulant is highly overall correlated with Blood Loss during surgeryHigh correlation
Antidiabetic is highly overall correlated with DiabetesMellitus and 1 other fieldsHigh correlation
Antihypertensive is highly overall correlated with Current Medication and 2 other fieldsHigh correlation
Antiplatelets is highly overall correlated with CAD History and 3 other fieldsHigh correlation
Antipsychotic is highly overall correlated with Neurological/ Psychological disease and 1 other fieldsHigh correlation
BUN before Discharge is highly overall correlated with Analgesics Post Operation and 18 other fieldsHigh correlation
BUN day 1 post surgery is highly overall correlated with BUN before Discharge and 17 other fieldsHigh correlation
Bacteremia is highly overall correlated with Pre Na and 5 other fieldsHigh correlation
BacteriaTypeRelatedToSurgery is highly overall correlated with Cardiac Complication and 10 other fieldsHigh correlation
Betablocker is highly overall correlated with Current Medication and 1 other fieldsHigh correlation
Blood Loss during surgery is highly overall correlated with AFib-tachycardia and 25 other fieldsHigh correlation
Blood Transfusion During Surgery is highly overall correlated with Blood Loss during surgery and 1 other fieldsHigh correlation
CAD History is highly overall correlated with Antiplatelets and 2 other fieldsHigh correlation
CKD is highly overall correlated with BUN before Discharge and 7 other fieldsHigh correlation
COPD is highly overall correlated with BUN before Discharge and 2 other fieldsHigh correlation
Cardiac Complication is highly overall correlated with BUN before Discharge and 10 other fieldsHigh correlation
Collection is highly overall correlated with BUN before Discharge and 11 other fieldsHigh correlation
Complication During Surgery is highly overall correlated with BUN before Discharge and 6 other fieldsHigh correlation
Complication Post Surgery is highly overall correlated with Infection of the surgical site and 5 other fieldsHigh correlation
Creatinine before Discharge is highly overall correlated with BUN before Discharge and 16 other fieldsHigh correlation
Creatinine_D1 is highly overall correlated with Antibiotics Post Operation and 10 other fieldsHigh correlation
Current Medication is highly overall correlated with Antihypertensive and 4 other fieldsHigh correlation
DiabetesMellitus is highly overall correlated with AntidiabeticHigh correlation
Dialysis is highly overall correlated with BUN before Discharge and 8 other fieldsHigh correlation
Diuratic is highly overall correlated with BUN day 1 post surgery and 2 other fieldsHigh correlation
Duration Of Surgery is highly overall correlated with hospital_stay_daysHigh correlation
Duration in intensive care unit (days) is highly overall correlated with BUN day 1 post surgery and 9 other fieldsHigh correlation
Dyslipidemia is highly overall correlated with Blood Loss during surgeryHigh correlation
ER Admission Before Surgery is highly overall correlated with Emergency Status of surgery and 2 other fieldsHigh correlation
Emergency Status of surgery is highly overall correlated with Blood Loss during surgery and 1 other fieldsHigh correlation
Extubation Post OR is highly overall correlated with Duration in intensive care unit (days) and 7 other fieldsHigh correlation
Floor is highly overall correlated with Neurological complication and 1 other fieldsHigh correlation
Gastrointestinal Disease is highly overall correlated with BUN day 1 post surgery and 5 other fieldsHigh correlation
HB before discharge is highly overall correlated with COPD and 10 other fieldsHigh correlation
HB day 1 post surgery is highly overall correlated with BUN before Discharge and 3 other fieldsHigh correlation
HF is highly overall correlated with AFib-tachycardia and 7 other fieldsHigh correlation
Hypertension is highly overall correlated with Antihypertensive and 5 other fieldsHigh correlation
Infection of the surgical site is highly overall correlated with Complication Post Surgery and 3 other fieldsHigh correlation
Na before Discharge is highly overall correlated with Analgesics Post Operation and 10 other fieldsHigh correlation
Na day 1 post surgery is highly overall correlated with Gastrointestinal Disease and 5 other fieldsHigh correlation
Neurological complication is highly overall correlated with BacteriaTypeRelatedToSurgery and 8 other fieldsHigh correlation
Neurological/ Psychological disease is highly overall correlated with Antipsychotic and 1 other fieldsHigh correlation
Number of ransfused packet cells is highly overall correlated with BUN before Discharge and 4 other fieldsHigh correlation
Number of transfused PC during surgery is highly overall correlated with Blood Loss during surgery and 1 other fieldsHigh correlation
Open heart surgery is highly overall correlated with AFib-tachycardia and 5 other fieldsHigh correlation
Other positive cultures related to surgery is highly overall correlated with BacteriaTypeRelatedToSurgery and 3 other fieldsHigh correlation
PAD is highly overall correlated with AFib-tachycardia and 5 other fieldsHigh correlation
Pathology Findings is highly overall correlated with Surgery and 1 other fieldsHigh correlation
Platelet befor eDischarge is highly overall correlated with Analgesics Post Operation and 8 other fieldsHigh correlation
Platelet day 1 post surgery is highly overall correlated with Platelet befor eDischarge and 1 other fieldsHigh correlation
Pre HB is highly overall correlated with HB before discharge and 4 other fieldsHigh correlation
Pre Na is highly overall correlated with Bacteremia and 5 other fieldsHigh correlation
Pre Platelet is highly overall correlated with Platelet day 1 post surgery and 2 other fieldsHigh correlation
Pre-BUN is highly overall correlated with BUN before Discharge and 12 other fieldsHigh correlation
Pre-Creatinine is highly overall correlated with BUN before Discharge and 9 other fieldsHigh correlation
Pulmonary complication is highly overall correlated with Duration in intensive care unit (days) and 2 other fieldsHigh correlation
Radiology is highly overall correlated with ER Admission Before Surgery and 1 other fieldsHigh correlation
Septic Shock is highly overall correlated with BUN before Discharge and 12 other fieldsHigh correlation
Stroke is highly overall correlated with BacteriaTypeRelatedToSurgery and 26 other fieldsHigh correlation
Surgery is highly overall correlated with CKD and 3 other fieldsHigh correlation
Thyroidal Medication is highly overall correlated with Type of Endocrine DiseaseHigh correlation
Tumor Category is highly overall correlated with Bacteremia and 22 other fieldsHigh correlation
Type of Endocrine Disease is highly overall correlated with Thyroidal MedicationHigh correlation
Type of Gastrointestinal Disease is highly overall correlated with BUN day 1 post surgery and 5 other fieldsHigh correlation
Type of Neurological/ psychological disease is highly overall correlated with Antipsychotic and 1 other fieldsHigh correlation
Type of bacteria at the surgical site is highly overall correlated with Collection and 19 other fieldsHigh correlation
Type of bacteria in blood is highly overall correlated with Bacteremia and 9 other fieldsHigh correlation
Type of cardiac Complication is highly overall correlated with BUN before Discharge and 10 other fieldsHigh correlation
Type of culture related to surgery is highly overall correlated with BacteriaTypeRelatedToSurgery and 13 other fieldsHigh correlation
Type of medical Imaging is highly overall correlated with ER Admission Before Surgery and 1 other fieldsHigh correlation
Type of nurologic complication is highly overall correlated with BacteriaTypeRelatedToSurgery and 9 other fieldsHigh correlation
Type of pulmonary complication is highly overall correlated with Cardiac Complication and 5 other fieldsHigh correlation
Type of stroke is highly overall correlated with BacteriaTypeRelatedToSurgery and 26 other fieldsHigh correlation
Unplanned transfer to intensive care unit is highly overall correlated with BUN day 1 post surgery and 8 other fieldsHigh correlation
Urine Output is highly overall correlated with Analgesics Post Operation and 19 other fieldsHigh correlation
Way Of Anesthesia is highly overall correlated with Blood Loss during surgery and 3 other fieldsHigh correlation
admission_other_hospital is highly overall correlated with BUN before Discharge and 10 other fieldsHigh correlation
age is highly overall correlated with Antiplatelets and 4 other fieldsHigh correlation
answered_call_followup is highly overall correlated with complication_post_discharge and 4 other fieldsHigh correlation
bmi is highly overall correlated with Creatinine before Discharge and 4 other fieldsHigh correlation
bmi_category is highly overall correlated with Stroke and 3 other fieldsHigh correlation
complication_post_discharge is highly overall correlated with BUN before Discharge and 11 other fieldsHigh correlation
death_in_hospital_postop is highly overall correlated with Blood Loss during surgery and 12 other fieldsHigh correlation
er_visit is highly overall correlated with Blood Loss during surgery and 9 other fieldsHigh correlation
gender is highly overall correlated with Floor and 1 other fieldsHigh correlation
height_cm is highly overall correlated with Stroke and 3 other fieldsHigh correlation
hospital_stay_days is highly overall correlated with Bacteremia and 6 other fieldsHigh correlation
id_number is highly overall correlated with patient_name and 1 other fieldsHigh correlation
infection_or_inflammation is highly overall correlated with Complication Post Surgery and 11 other fieldsHigh correlation
other_complication is highly overall correlated with post_discharge_complication_type and 4 other fieldsHigh correlation
patient_name is highly overall correlated with id_number and 1 other fieldsHigh correlation
physician_name is highly overall correlated with id_number and 1 other fieldsHigh correlation
post_discharge_complication_type is highly overall correlated with Bacteremia and 23 other fieldsHigh correlation
readmission_related_to_or is highly overall correlated with Complication Post Surgery and 13 other fieldsHigh correlation
redo_surgery is highly overall correlated with BUN before Discharge and 22 other fieldsHigh correlation
unplanned_or_reason is highly overall correlated with Collection and 13 other fieldsHigh correlation
unplanned_return_to_or is highly overall correlated with Type of bacteria at the surgical site and 3 other fieldsHigh correlation
weight_kg is highly overall correlated with Creatinine before Discharge and 5 other fieldsHigh correlation
Dyslipidemia is highly imbalanced (54.5%)Imbalance
HF is highly imbalanced (90.6%)Imbalance
Open heart surgery is highly imbalanced (90.1%)Imbalance
AFib-tachycardia is highly imbalanced (88.1%)Imbalance
PAD is highly imbalanced (91.2%)Imbalance
COPD is highly imbalanced (71.4%)Imbalance
CKD is highly imbalanced (66.7%)Imbalance
Dialysis is highly imbalanced (77.4%)Imbalance
Neurological/ Psychological disease is highly imbalanced (58.3%)Imbalance
Type of Neurological/ psychological disease is highly imbalanced (81.3%)Imbalance
Gastrointestinal Disease is highly imbalanced (85.3%)Imbalance
Type of Gastrointestinal Disease is highly imbalanced (92.6%)Imbalance
Type of Endocrine Disease is highly imbalanced (83.5%)Imbalance
Anticoagulant is highly imbalanced (70.6%)Imbalance
Thyroidal Medication is highly imbalanced (66.0%)Imbalance
Antipsychotic is highly imbalanced (61.7%)Imbalance
Diuratic is highly imbalanced (75.7%)Imbalance
OtherMedication is highly imbalanced (90.8%)Imbalance
Type of medical Imaging is highly imbalanced (54.2%)Imbalance
Emergency Status of surgery is highly imbalanced (55.1%)Imbalance
Anesthesia type is highly imbalanced (53.9%)Imbalance
Way Of Anesthesia is highly imbalanced (83.3%)Imbalance
Complication During Surgery is highly imbalanced (97.0%)Imbalance
Blood Transfusion During Surgery is highly imbalanced (74.8%)Imbalance
Number of transfused PC during surgery is highly imbalanced (86.2%)Imbalance
Complication Post Surgery is highly imbalanced (63.1%)Imbalance
Cardiac Complication is highly imbalanced (98.0%)Imbalance
Type of cardiac Complication is highly imbalanced (98.0%)Imbalance
Pulmonary complication is highly imbalanced (89.7%)Imbalance
Type of pulmonary complication is highly imbalanced (93.9%)Imbalance
Number of ransfused packet cells is highly imbalanced (94.6%)Imbalance
Neurological complication is highly imbalanced (96.4%)Imbalance
Type of nurologic complication is highly imbalanced (97.5%)Imbalance
Stroke is highly imbalanced (98.0%)Imbalance
Type of stroke is highly imbalanced (98.0%)Imbalance
Infection of the surgical site is highly imbalanced (83.2%)Imbalance
Type of bacteria at the surgical site is highly imbalanced (93.2%)Imbalance
Bacteremia is highly imbalanced (93.5%)Imbalance
Type of bacteria in blood is highly imbalanced (96.5%)Imbalance
Other positive cultures related to surgery is highly imbalanced (93.5%)Imbalance
Type of culture related to surgery is highly imbalanced (96.2%)Imbalance
BacteriaTypeRelatedToSurgery is highly imbalanced (96.5%)Imbalance
Unplanned transfer to intensive care unit is highly imbalanced (96.4%)Imbalance
Duration in intensive care unit (days) is highly imbalanced (97.5%)Imbalance
Septic Shock is highly imbalanced (96.4%)Imbalance
Collection is highly imbalanced (96.4%)Imbalance
unplanned_return_to_or is highly imbalanced (86.3%)Imbalance
unplanned_or_reason is highly imbalanced (93.5%)Imbalance
other_complication is highly imbalanced (93.1%)Imbalance
death_in_hospital_postop is highly imbalanced (93.5%)Imbalance
complication_post_discharge is highly imbalanced (69.3%)Imbalance
er_visit is highly imbalanced (81.0%)Imbalance
post_discharge_complication_type is highly imbalanced (86.9%)Imbalance
readmission_related_to_or is highly imbalanced (79.4%)Imbalance
infection_or_inflammation is highly imbalanced (74.9%)Imbalance
redo_surgery is highly imbalanced (96.9%)Imbalance
admission_other_hospital is highly imbalanced (96.9%)Imbalance
weight_kg has 27 (5.2%) missing valuesMissing
height_cm has 34 (6.5%) missing valuesMissing
bmi has 34 (6.5%) missing valuesMissing
bmi_category has 34 (6.5%) missing valuesMissing
blood_group has 456 (87.4%) missing valuesMissing
ICD10 has 215 (41.2%) missing valuesMissing
Pre-BUN has 200 (38.3%) missing valuesMissing
BUN day 1 post surgery has 461 (88.3%) missing valuesMissing
BUN before Discharge has 489 (93.7%) missing valuesMissing
Pre-Creatinine has 106 (20.3%) missing valuesMissing
Creatinine_D1 has 440 (84.3%) missing valuesMissing
Creatinine before Discharge has 475 (91.0%) missing valuesMissing
Pre Na has 187 (35.8%) missing valuesMissing
Na day 1 post surgery has 437 (83.7%) missing valuesMissing
Na before Discharge has 466 (89.3%) missing valuesMissing
Pre HB has 10 (1.9%) missing valuesMissing
HB day 1 post surgery has 401 (76.8%) missing valuesMissing
HB before discharge has 454 (87.0%) missing valuesMissing
Pre Platelet has 11 (2.1%) missing valuesMissing
Platelet day 1 post surgery has 401 (76.8%) missing valuesMissing
Platelet befor eDischarge has 454 (87.0%) missing valuesMissing
Duration Of Surgery has 6 (1.1%) missing valuesMissing
Extubation Post OR has 240 (46.0%) missing valuesMissing
Blood Loss during surgery has 488 (93.5%) missing valuesMissing
Urine Output has 488 (93.5%) missing valuesMissing
Tumor Category has 454 (87.0%) missing valuesMissing
Pathology description has 237 (45.4%) missing valuesMissing
Anesthesia type has 8 (1.5%) missing valuesMissing
Way Of Anesthesia has 8 (1.5%) missing valuesMissing
complication_post_discharge has 212 (40.6%) missing valuesMissing
er_visit has 212 (40.6%) missing valuesMissing
post_discharge_complication_type has 212 (40.6%) missing valuesMissing
readmission_related_to_or has 212 (40.6%) missing valuesMissing
infection_or_inflammation has 212 (40.6%) missing valuesMissing
redo_surgery has 212 (40.6%) missing valuesMissing
admission_other_hospital has 212 (40.6%) missing valuesMissing
death_post_discharge has 212 (40.6%) missing valuesMissing
notes_description has 464 (88.9%) missing valuesMissing
id_number is uniformly distributedUniform
patient_name is uniformly distributedUniform
id_number has unique valuesUnique
patient_name has unique valuesUnique
marital_status is an unsupported type, check if it needs cleaning or further analysisUnsupported
blood_group is an unsupported type, check if it needs cleaning or further analysisUnsupported
Allergy is an unsupported type, check if it needs cleaning or further analysisUnsupported
Endocrine Disease is an unsupported type, check if it needs cleaning or further analysisUnsupported
Cancer is an unsupported type, check if it needs cleaning or further analysisUnsupported
Type of cancer is an unsupported type, check if it needs cleaning or further analysisUnsupported
Other_History is an unsupported type, check if it needs cleaning or further analysisUnsupported
Code of surgeon is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2025-12-18 11:44:41.937201
Analysis finished2025-12-18 11:46:35.706858
Duration1 minute and 53.77 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

id_number
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct522
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7261.5
Minimum7001
Maximum7522
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2025-12-18T11:46:35.814676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7001
5-th percentile7027.05
Q17131.25
median7261.5
Q37391.75
95-th percentile7495.95
Maximum7522
Range521
Interquartile range (IQR)260.5

Descriptive statistics

Standard deviation150.83269
Coefficient of variation (CV)0.020771561
Kurtosis-1.2
Mean7261.5
Median Absolute Deviation (MAD)130.5
Skewness0
Sum3790503
Variance22750.5
MonotonicityNot monotonic
2025-12-18T11:46:35.952356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73831
 
0.2%
71651
 
0.2%
74281
 
0.2%
70691
 
0.2%
70681
 
0.2%
74711
 
0.2%
74731
 
0.2%
70701
 
0.2%
73931
 
0.2%
70721
 
0.2%
Other values (512)512
98.1%
ValueCountFrequency (%)
70011
0.2%
70021
0.2%
70031
0.2%
70041
0.2%
70051
0.2%
70061
0.2%
70071
0.2%
70081
0.2%
70091
0.2%
70101
0.2%
ValueCountFrequency (%)
75221
0.2%
75211
0.2%
75201
0.2%
75191
0.2%
75181
0.2%
75171
0.2%
75161
0.2%
75151
0.2%
75141
0.2%
75131
0.2%

patient_name
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct522
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1061.5
Minimum801
Maximum1322
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2025-12-18T11:46:36.087628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum801
5-th percentile827.05
Q1931.25
median1061.5
Q31191.75
95-th percentile1295.95
Maximum1322
Range521
Interquartile range (IQR)260.5

Descriptive statistics

Standard deviation150.83269
Coefficient of variation (CV)0.14209391
Kurtosis-1.2
Mean1061.5
Median Absolute Deviation (MAD)130.5
Skewness0
Sum554103
Variance22750.5
MonotonicityNot monotonic
2025-12-18T11:46:36.220562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11831
 
0.2%
9651
 
0.2%
12281
 
0.2%
8691
 
0.2%
8681
 
0.2%
12711
 
0.2%
12731
 
0.2%
8701
 
0.2%
11931
 
0.2%
8721
 
0.2%
Other values (512)512
98.1%
ValueCountFrequency (%)
8011
0.2%
8021
0.2%
8031
0.2%
8041
0.2%
8051
0.2%
8061
0.2%
8071
0.2%
8081
0.2%
8091
0.2%
8101
0.2%
ValueCountFrequency (%)
13221
0.2%
13211
0.2%
13201
0.2%
13191
0.2%
13181
0.2%
13171
0.2%
13161
0.2%
13151
0.2%
13141
0.2%
13131
0.2%

phone_number
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
89000001
522 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters4176
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row89000001
2nd row89000001
3rd row89000001
4th row89000001
5th row89000001

Common Values

ValueCountFrequency (%)
89000001522
100.0%

Length

2025-12-18T11:46:36.346928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T11:46:36.433056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
89000001522
100.0%

Most occurring characters

ValueCountFrequency (%)
02610
62.5%
8522
 
12.5%
9522
 
12.5%
1522
 
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)4176
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02610
62.5%
8522
 
12.5%
9522
 
12.5%
1522
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4176
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02610
62.5%
8522
 
12.5%
9522
 
12.5%
1522
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4176
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02610
62.5%
8522
 
12.5%
9522
 
12.5%
1522
 
12.5%

physician_name
Real number (ℝ)

High correlation 

Distinct67
Distinct (%)12.9%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean24.763916
Minimum1
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2025-12-18T11:46:36.514297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q112
median22
Q334
95-th percentile55
Maximum72
Range71
Interquartile range (IQR)22

Descriptive statistics

Standard deviation15.989417
Coefficient of variation (CV)0.64567403
Kurtosis-0.059301349
Mean24.763916
Median Absolute Deviation (MAD)12
Skewness0.50898859
Sum12902
Variance255.66146
MonotonicityNot monotonic
2025-12-18T11:46:36.643215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2077
 
14.8%
232
 
6.1%
128
 
5.4%
927
 
5.2%
4626
 
5.0%
1525
 
4.8%
3420
 
3.8%
2118
 
3.4%
2817
 
3.3%
3116
 
3.1%
Other values (57)235
45.0%
ValueCountFrequency (%)
128
5.4%
232
6.1%
35
 
1.0%
41
 
0.2%
51
 
0.2%
66
 
1.1%
78
 
1.5%
814
2.7%
927
5.2%
102
 
0.4%
ValueCountFrequency (%)
721
 
0.2%
711
 
0.2%
701
 
0.2%
692
0.4%
681
 
0.2%
671
 
0.2%
661
 
0.2%
651
 
0.2%
642
0.4%
633
0.6%
Distinct37
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
Minimum2024-12-03 00:00:00
Maximum2025-06-13 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-12-18T11:46:36.764594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:36.893304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)

age
Real number (ℝ)

High correlation 

Distinct89
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.632663
Minimum0.25
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2025-12-18T11:46:37.022585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.25
5-th percentile4
Q128
median44
Q359
95-th percentile75
Maximum93
Range92.75
Interquartile range (IQR)31

Descriptive statistics

Standard deviation21.831479
Coefficient of variation (CV)0.51208341
Kurtosis-0.82276604
Mean42.632663
Median Absolute Deviation (MAD)16
Skewness-0.1777198
Sum22254.25
Variance476.61348
MonotonicityNot monotonic
2025-12-18T11:46:37.151704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5315
 
2.9%
4414
 
2.7%
5014
 
2.7%
4712
 
2.3%
3112
 
2.3%
5812
 
2.3%
5112
 
2.3%
312
 
2.3%
3310
 
1.9%
6010
 
1.9%
Other values (79)399
76.4%
ValueCountFrequency (%)
0.251
 
0.2%
0.52
 
0.4%
12
 
0.4%
24
 
0.8%
312
2.3%
49
1.7%
58
1.5%
69
1.7%
74
 
0.8%
83
 
0.6%
ValueCountFrequency (%)
931
 
0.2%
891
 
0.2%
881
 
0.2%
871
 
0.2%
861
 
0.2%
835
1.0%
821
 
0.2%
811
 
0.2%
802
 
0.4%
792
 
0.4%

gender
Categorical

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
male
298 
female
224 

Length

Max length6
Median length4
Mean length4.8582375
Min length4

Characters and Unicode

Total characters2536
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowmale
3rd rowmale
4th rowfemale
5th rowfemale

Common Values

ValueCountFrequency (%)
male298
57.1%
female224
42.9%

Length

2025-12-18T11:46:37.279679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T11:46:37.350958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male298
57.1%
female224
42.9%

Most occurring characters

ValueCountFrequency (%)
e746
29.4%
m522
20.6%
a522
20.6%
l522
20.6%
f224
 
8.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)2536
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e746
29.4%
m522
20.6%
a522
20.6%
l522
20.6%
f224
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2536
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e746
29.4%
m522
20.6%
a522
20.6%
l522
20.6%
f224
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2536
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e746
29.4%
m522
20.6%
a522
20.6%
l522
20.6%
f224
 
8.8%

governorate
Categorical

Distinct6
Distinct (%)1.2%
Missing4
Missing (%)0.8%
Memory size4.2 KiB
Mount Lebanon
351 
South
64 
Beqaa
49 
Beirut
 
28
non lebanese
 
23

Length

Max length13
Median length13
Mean length10.785714
Min length5

Characters and Unicode

Total characters5587
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBeqaa
2nd rowMount Lebanon
3rd rowBeirut
4th rowMount Lebanon
5th rownon lebanese

Common Values

ValueCountFrequency (%)
Mount Lebanon351
67.2%
South64
 
12.3%
Beqaa49
 
9.4%
Beirut28
 
5.4%
non lebanese23
 
4.4%
North3
 
0.6%
(Missing)4
 
0.8%

Length

2025-12-18T11:46:37.457205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T11:46:37.547845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mount351
39.3%
lebanon351
39.3%
south64
 
7.2%
beqaa49
 
5.5%
beirut28
 
3.1%
non23
 
2.6%
lebanese23
 
2.6%
north3
 
0.3%

Most occurring characters

ValueCountFrequency (%)
n1122
20.1%
o792
14.2%
e497
8.9%
a472
8.4%
t446
 
8.0%
u443
 
7.9%
b374
 
6.7%
374
 
6.7%
M351
 
6.3%
L351
 
6.3%
Other values (9)365
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)5587
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n1122
20.1%
o792
14.2%
e497
8.9%
a472
8.4%
t446
 
8.0%
u443
 
7.9%
b374
 
6.7%
374
 
6.7%
M351
 
6.3%
L351
 
6.3%
Other values (9)365
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5587
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n1122
20.1%
o792
14.2%
e497
8.9%
a472
8.4%
t446
 
8.0%
u443
 
7.9%
b374
 
6.7%
374
 
6.7%
M351
 
6.3%
L351
 
6.3%
Other values (9)365
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5587
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n1122
20.1%
o792
14.2%
e497
8.9%
a472
8.4%
t446
 
8.0%
u443
 
7.9%
b374
 
6.7%
374
 
6.7%
M351
 
6.3%
L351
 
6.3%
Other values (9)365
 
6.5%

marital_status
Unsupported

Rejected  Unsupported 

Missing2
Missing (%)0.4%
Memory size4.2 KiB

insurance_type
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
government
373 
private
109 
ensurance
40 

Length

Max length10
Median length10
Mean length9.2969349
Min length7

Characters and Unicode

Total characters4853
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgovernment
2nd rowgovernment
3rd rowprivate
4th rowgovernment
5th rowprivate

Common Values

ValueCountFrequency (%)
government373
71.5%
private109
 
20.9%
ensurance40
 
7.7%

Length

2025-12-18T11:46:37.657119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T11:46:37.729463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
government373
71.5%
private109
 
20.9%
ensurance40
 
7.7%

Most occurring characters

ValueCountFrequency (%)
e935
19.3%
n826
17.0%
r522
10.8%
v482
9.9%
t482
9.9%
o373
 
7.7%
g373
 
7.7%
m373
 
7.7%
a149
 
3.1%
p109
 
2.2%
Other values (4)229
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)4853
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e935
19.3%
n826
17.0%
r522
10.8%
v482
9.9%
t482
9.9%
o373
 
7.7%
g373
 
7.7%
m373
 
7.7%
a149
 
3.1%
p109
 
2.2%
Other values (4)229
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4853
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e935
19.3%
n826
17.0%
r522
10.8%
v482
9.9%
t482
9.9%
o373
 
7.7%
g373
 
7.7%
m373
 
7.7%
a149
 
3.1%
p109
 
2.2%
Other values (4)229
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4853
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e935
19.3%
n826
17.0%
r522
10.8%
v482
9.9%
t482
9.9%
o373
 
7.7%
g373
 
7.7%
m373
 
7.7%
a149
 
3.1%
p109
 
2.2%
Other values (4)229
 
4.7%

weight_kg
Real number (ℝ)

High correlation  Missing 

Distinct129
Distinct (%)26.1%
Missing27
Missing (%)5.2%
Infinite0
Infinite (%)0.0%
Mean72.186727
Minimum5.4
Maximum161
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2025-12-18T11:46:37.828035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5.4
5-th percentile16
Q160
median75
Q389
95-th percentile111.3
Maximum161
Range155.6
Interquartile range (IQR)29

Descriptive statistics

Standard deviation27.332607
Coefficient of variation (CV)0.37863758
Kurtosis0.42499223
Mean72.186727
Median Absolute Deviation (MAD)15
Skewness-0.34305414
Sum35732.43
Variance747.07143
MonotonicityNot monotonic
2025-12-18T11:46:39.072139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8020
 
3.8%
9018
 
3.4%
7516
 
3.1%
8414
 
2.7%
7013
 
2.5%
7213
 
2.5%
8513
 
2.5%
6012
 
2.3%
6211
 
2.1%
7811
 
2.1%
Other values (119)354
67.8%
(Missing)27
 
5.2%
ValueCountFrequency (%)
5.41
0.2%
5.71
0.2%
8.31
0.2%
8.431
0.2%
91
0.2%
9.41
0.2%
101
0.2%
10.61
0.2%
111
0.2%
122
0.4%
ValueCountFrequency (%)
1611
 
0.2%
1502
0.4%
1401
 
0.2%
1371
 
0.2%
1362
0.4%
1304
0.8%
1251
 
0.2%
1241
 
0.2%
1232
0.4%
1181
 
0.2%

height_cm
Real number (ℝ)

High correlation  Missing 

Distinct81
Distinct (%)16.6%
Missing34
Missing (%)6.5%
Infinite0
Infinite (%)0.0%
Mean160.66189
Minimum11
Maximum201
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2025-12-18T11:46:39.285610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile102
Q1157
median166
Q3175
95-th percentile185
Maximum201
Range190
Interquartile range (IQR)18

Descriptive statistics

Standard deviation24.648961
Coefficient of variation (CV)0.15342134
Kurtosis5.9446647
Mean160.66189
Median Absolute Deviation (MAD)9
Skewness-2.2295215
Sum78403
Variance607.57128
MonotonicityNot monotonic
2025-12-18T11:46:39.484023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16034
 
6.5%
17032
 
6.1%
16524
 
4.6%
17523
 
4.4%
18020
 
3.8%
17217
 
3.3%
17616
 
3.1%
16415
 
2.9%
15614
 
2.7%
17314
 
2.7%
Other values (71)279
53.4%
(Missing)34
 
6.5%
ValueCountFrequency (%)
111
 
0.2%
581
 
0.2%
611
 
0.2%
641
 
0.2%
703
0.6%
781
 
0.2%
851
 
0.2%
871
 
0.2%
882
0.4%
902
0.4%
ValueCountFrequency (%)
2011
 
0.2%
1942
 
0.4%
1931
 
0.2%
1921
 
0.2%
1904
0.8%
1882
 
0.4%
1876
1.1%
1865
1.0%
1855
1.0%
1843
0.6%

bmi
Real number (ℝ)

High correlation  Missing 

Distinct223
Distinct (%)45.7%
Missing34
Missing (%)6.5%
Infinite0
Infinite (%)0.0%
Mean26.790369
Minimum11.9
Maximum57.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2025-12-18T11:46:39.705096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11.9
5-th percentile15.87
Q122.2
median26.6
Q331.1
95-th percentile38.065
Maximum57.8
Range45.9
Interquartile range (IQR)8.9

Descriptive statistics

Standard deviation6.8320366
Coefficient of variation (CV)0.25501839
Kurtosis0.91985288
Mean26.790369
Median Absolute Deviation (MAD)4.4
Skewness0.47181484
Sum13073.7
Variance46.676724
MonotonicityNot monotonic
2025-12-18T11:46:39.905833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.68
 
1.5%
33.17
 
1.3%
30.87
 
1.3%
25.56
 
1.1%
28.16
 
1.1%
27.36
 
1.1%
21.56
 
1.1%
27.76
 
1.1%
24.56
 
1.1%
30.46
 
1.1%
Other values (213)424
81.2%
(Missing)34
 
6.5%
ValueCountFrequency (%)
11.91
0.2%
12.11
0.2%
12.52
0.4%
12.71
0.2%
13.21
0.2%
13.51
0.2%
13.62
0.4%
141
0.2%
14.51
0.2%
14.61
0.2%
ValueCountFrequency (%)
57.81
 
0.2%
51.81
 
0.2%
50.81
 
0.2%
47.11
 
0.2%
44.91
 
0.2%
43.91
 
0.2%
42.81
 
0.2%
42.73
0.6%
42.21
 
0.2%
421
 
0.2%

bmi_category
Categorical

High correlation  Missing 

Distinct4
Distinct (%)0.8%
Missing34
Missing (%)6.5%
Memory size4.2 KiB
obese
152 
overweight
140 
normal
138 
underweight
58 

Length

Max length11
Median length10
Mean length7.4303279
Min length5

Characters and Unicode

Total characters3626
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowunderweight
2nd rownormal
3rd rownormal
4th rownormal
5th rowoverweight

Common Values

ValueCountFrequency (%)
obese152
29.1%
overweight140
26.8%
normal138
26.4%
underweight58
 
11.1%
(Missing)34
 
6.5%

Length

2025-12-18T11:46:40.085515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T11:46:40.197918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
obese152
31.1%
overweight140
28.7%
normal138
28.3%
underweight58
 
11.9%

Most occurring characters

ValueCountFrequency (%)
e700
19.3%
o430
11.9%
r336
9.3%
w198
 
5.5%
i198
 
5.5%
g198
 
5.5%
h198
 
5.5%
t198
 
5.5%
n196
 
5.4%
b152
 
4.2%
Other values (7)822
22.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)3626
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e700
19.3%
o430
11.9%
r336
9.3%
w198
 
5.5%
i198
 
5.5%
g198
 
5.5%
h198
 
5.5%
t198
 
5.5%
n196
 
5.4%
b152
 
4.2%
Other values (7)822
22.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3626
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e700
19.3%
o430
11.9%
r336
9.3%
w198
 
5.5%
i198
 
5.5%
g198
 
5.5%
h198
 
5.5%
t198
 
5.5%
n196
 
5.4%
b152
 
4.2%
Other values (7)822
22.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3626
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e700
19.3%
o430
11.9%
r336
9.3%
w198
 
5.5%
i198
 
5.5%
g198
 
5.5%
h198
 
5.5%
t198
 
5.5%
n196
 
5.4%
b152
 
4.2%
Other values (7)822
22.7%

blood_group
Unsupported

Missing  Rejected  Unsupported 

Missing456
Missing (%)87.4%
Memory size4.2 KiB

smoking_status
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
no smoking
269 
smoking
248 
ex-smoking
 
5

Length

Max length10
Median length10
Mean length8.5747126
Min length7

Characters and Unicode

Total characters4476
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno smoking
2nd rowsmoking
3rd rowno smoking
4th rowno smoking
5th rowno smoking

Common Values

ValueCountFrequency (%)
no smoking269
51.5%
smoking248
47.5%
ex-smoking5
 
1.0%

Length

2025-12-18T11:46:40.346353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T11:46:40.449118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
smoking517
65.4%
no269
34.0%
ex-smoking5
 
0.6%

Most occurring characters

ValueCountFrequency (%)
n791
17.7%
o791
17.7%
s522
11.7%
g522
11.7%
m522
11.7%
k522
11.7%
i522
11.7%
269
 
6.0%
e5
 
0.1%
x5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)4476
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n791
17.7%
o791
17.7%
s522
11.7%
g522
11.7%
m522
11.7%
k522
11.7%
i522
11.7%
269
 
6.0%
e5
 
0.1%
x5
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4476
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n791
17.7%
o791
17.7%
s522
11.7%
g522
11.7%
m522
11.7%
k522
11.7%
i522
11.7%
269
 
6.0%
e5
 
0.1%
x5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4476
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n791
17.7%
o791
17.7%
s522
11.7%
g522
11.7%
m522
11.7%
k522
11.7%
i522
11.7%
269
 
6.0%
e5
 
0.1%
x5
 
0.1%
Distinct170
Distinct (%)32.6%
Missing1
Missing (%)0.2%
Memory size4.2 KiB
2025-12-18T11:46:40.765086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length39
Median length30
Mean length10.053743
Min length1

Characters and Unicode

Total characters5238
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique114 ?
Unique (%)21.9%

Sample

1st row0
2nd rownephrectomy
3rd rowcraniotomy
4th row0
5th row0
ValueCountFrequency (%)
0125
 
15.1%
surgery64
 
7.7%
left40
 
4.8%
c/s29
 
3.5%
hernia27
 
3.3%
right26
 
3.1%
cholecystectomy23
 
2.8%
excision17
 
2.1%
smr16
 
1.9%
cyst13
 
1.6%
Other values (161)449
54.2%
2025-12-18T11:46:41.312441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e494
 
9.4%
t409
 
7.8%
r374
 
7.1%
o332
 
6.3%
308
 
5.9%
a304
 
5.8%
i289
 
5.5%
y281
 
5.4%
c280
 
5.3%
s268
 
5.1%
Other values (39)1899
36.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)5238
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e494
 
9.4%
t409
 
7.8%
r374
 
7.1%
o332
 
6.3%
308
 
5.9%
a304
 
5.8%
i289
 
5.5%
y281
 
5.4%
c280
 
5.3%
s268
 
5.1%
Other values (39)1899
36.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5238
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e494
 
9.4%
t409
 
7.8%
r374
 
7.1%
o332
 
6.3%
308
 
5.9%
a304
 
5.8%
i289
 
5.5%
y281
 
5.4%
c280
 
5.3%
s268
 
5.1%
Other values (39)1899
36.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5238
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e494
 
9.4%
t409
 
7.8%
r374
 
7.1%
o332
 
6.3%
308
 
5.9%
a304
 
5.8%
i289
 
5.5%
y281
 
5.4%
c280
 
5.3%
s268
 
5.1%
Other values (39)1899
36.3%

Allergy
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size4.2 KiB

Hypertension
Boolean

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
374 
True
148 
ValueCountFrequency (%)
False374
71.6%
True148
 
28.4%
2025-12-18T11:46:41.431456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

DiabetesMellitus
Boolean

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
441 
True
81 
ValueCountFrequency (%)
False441
84.5%
True81
 
15.5%
2025-12-18T11:46:41.509509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Dyslipidemia
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
472 
True
50 
ValueCountFrequency (%)
False472
90.4%
True50
 
9.6%
2025-12-18T11:46:41.558502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

CAD History
Boolean

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
447 
True
75 
ValueCountFrequency (%)
False447
85.6%
True75
 
14.4%
2025-12-18T11:46:41.605614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

HF
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
no
509 
yes
 
11
CABG
 
1
HF
 
1

Length

Max length4
Median length2
Mean length2.0249042
Min length2

Characters and Unicode

Total characters1057
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no509
97.5%
yes11
 
2.1%
CABG1
 
0.2%
HF1
 
0.2%

Length

2025-12-18T11:46:41.698442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T11:46:41.790202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no509
97.5%
yes11
 
2.1%
cabg1
 
0.2%
hf1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n509
48.2%
o509
48.2%
y11
 
1.0%
e11
 
1.0%
s11
 
1.0%
C1
 
0.1%
A1
 
0.1%
B1
 
0.1%
G1
 
0.1%
H1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1057
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n509
48.2%
o509
48.2%
y11
 
1.0%
e11
 
1.0%
s11
 
1.0%
C1
 
0.1%
A1
 
0.1%
B1
 
0.1%
G1
 
0.1%
H1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1057
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n509
48.2%
o509
48.2%
y11
 
1.0%
e11
 
1.0%
s11
 
1.0%
C1
 
0.1%
A1
 
0.1%
B1
 
0.1%
G1
 
0.1%
H1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1057
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n509
48.2%
o509
48.2%
y11
 
1.0%
e11
 
1.0%
s11
 
1.0%
C1
 
0.1%
A1
 
0.1%
B1
 
0.1%
G1
 
0.1%
H1
 
0.1%

Open heart surgery
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
no
508 
yes
 
12
CABG
 
1
HF
 
1

Length

Max length4
Median length2
Mean length2.0268199
Min length2

Characters and Unicode

Total characters1058
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no508
97.3%
yes12
 
2.3%
CABG1
 
0.2%
HF1
 
0.2%

Length

2025-12-18T11:46:41.885332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T11:46:41.966015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no508
97.3%
yes12
 
2.3%
cabg1
 
0.2%
hf1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n508
48.0%
o508
48.0%
y12
 
1.1%
e12
 
1.1%
s12
 
1.1%
C1
 
0.1%
A1
 
0.1%
B1
 
0.1%
G1
 
0.1%
H1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1058
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n508
48.0%
o508
48.0%
y12
 
1.1%
e12
 
1.1%
s12
 
1.1%
C1
 
0.1%
A1
 
0.1%
B1
 
0.1%
G1
 
0.1%
H1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1058
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n508
48.0%
o508
48.0%
y12
 
1.1%
e12
 
1.1%
s12
 
1.1%
C1
 
0.1%
A1
 
0.1%
B1
 
0.1%
G1
 
0.1%
H1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1058
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n508
48.0%
o508
48.0%
y12
 
1.1%
e12
 
1.1%
s12
 
1.1%
C1
 
0.1%
A1
 
0.1%
B1
 
0.1%
G1
 
0.1%
H1
 
0.1%

AFib-tachycardia
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
no
504 
yes
 
16
CABG
 
1
HF
 
1

Length

Max length4
Median length2
Mean length2.0344828
Min length2

Characters and Unicode

Total characters1062
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no504
96.6%
yes16
 
3.1%
CABG1
 
0.2%
HF1
 
0.2%

Length

2025-12-18T11:46:42.063745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T11:46:42.141527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no504
96.6%
yes16
 
3.1%
cabg1
 
0.2%
hf1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n504
47.5%
o504
47.5%
y16
 
1.5%
e16
 
1.5%
s16
 
1.5%
C1
 
0.1%
A1
 
0.1%
B1
 
0.1%
G1
 
0.1%
H1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n504
47.5%
o504
47.5%
y16
 
1.5%
e16
 
1.5%
s16
 
1.5%
C1
 
0.1%
A1
 
0.1%
B1
 
0.1%
G1
 
0.1%
H1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n504
47.5%
o504
47.5%
y16
 
1.5%
e16
 
1.5%
s16
 
1.5%
C1
 
0.1%
A1
 
0.1%
B1
 
0.1%
G1
 
0.1%
H1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n504
47.5%
o504
47.5%
y16
 
1.5%
e16
 
1.5%
s16
 
1.5%
C1
 
0.1%
A1
 
0.1%
B1
 
0.1%
G1
 
0.1%
H1
 
0.1%

PAD
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
no
510 
yes
 
10
CABG
 
1
HF
 
1

Length

Max length4
Median length2
Mean length2.0229885
Min length2

Characters and Unicode

Total characters1056
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no510
97.7%
yes10
 
1.9%
CABG1
 
0.2%
HF1
 
0.2%

Length

2025-12-18T11:46:42.241502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T11:46:42.328666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no510
97.7%
yes10
 
1.9%
cabg1
 
0.2%
hf1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n510
48.3%
o510
48.3%
y10
 
0.9%
e10
 
0.9%
s10
 
0.9%
C1
 
0.1%
A1
 
0.1%
B1
 
0.1%
G1
 
0.1%
H1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1056
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n510
48.3%
o510
48.3%
y10
 
0.9%
e10
 
0.9%
s10
 
0.9%
C1
 
0.1%
A1
 
0.1%
B1
 
0.1%
G1
 
0.1%
H1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1056
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n510
48.3%
o510
48.3%
y10
 
0.9%
e10
 
0.9%
s10
 
0.9%
C1
 
0.1%
A1
 
0.1%
B1
 
0.1%
G1
 
0.1%
H1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1056
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n510
48.3%
o510
48.3%
y10
 
0.9%
e10
 
0.9%
s10
 
0.9%
C1
 
0.1%
A1
 
0.1%
B1
 
0.1%
G1
 
0.1%
H1
 
0.1%

COPD
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
496 
True
 
26
ValueCountFrequency (%)
False496
95.0%
True26
 
5.0%
2025-12-18T11:46:42.382969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

CKD
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
490 
True
 
32
ValueCountFrequency (%)
False490
93.9%
True32
 
6.1%
2025-12-18T11:46:42.426912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Dialysis
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
503 
True
 
19
ValueCountFrequency (%)
False503
96.4%
True19
 
3.6%
2025-12-18T11:46:42.471515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Neurological/ Psychological disease
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
478 
True
 
44
ValueCountFrequency (%)
False478
91.6%
True44
 
8.4%
2025-12-18T11:46:42.516852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Type of Neurological/ psychological disease
Categorical

High correlation  Imbalance 

Distinct12
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
no
477 
epilepsy
 
12
CVA
 
10
migraine
 
9
anxiety
 
3
Other values (7)
 
11

Length

Max length21
Median length2
Mean length2.5153257
Min length2

Characters and Unicode

Total characters1313
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.8%

Sample

1st rowno
2nd rowno
3rd rowepilepsy
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no477
91.4%
epilepsy12
 
2.3%
CVA10
 
1.9%
migraine9
 
1.7%
anxiety3
 
0.6%
depression3
 
0.6%
parkinson disease2
 
0.4%
migrain2
 
0.4%
anxiety-fibromyalgia1
 
0.2%
nono1
 
0.2%
Other values (2)2
 
0.4%

Length

2025-12-18T11:46:42.607083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
no477
90.7%
epilepsy12
 
2.3%
cva10
 
1.9%
migraine9
 
1.7%
anxiety3
 
0.6%
depression3
 
0.6%
disease3
 
0.6%
parkinson2
 
0.4%
migrain2
 
0.4%
anxiety-fibromyalgia1
 
0.2%
Other values (4)4
 
0.8%

Most occurring characters

ValueCountFrequency (%)
n501
38.2%
o485
36.9%
e55
 
4.2%
i51
 
3.9%
p29
 
2.2%
s28
 
2.1%
a25
 
1.9%
r18
 
1.4%
y17
 
1.3%
l15
 
1.1%
Other values (16)89
 
6.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1313
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n501
38.2%
o485
36.9%
e55
 
4.2%
i51
 
3.9%
p29
 
2.2%
s28
 
2.1%
a25
 
1.9%
r18
 
1.4%
y17
 
1.3%
l15
 
1.1%
Other values (16)89
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1313
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n501
38.2%
o485
36.9%
e55
 
4.2%
i51
 
3.9%
p29
 
2.2%
s28
 
2.1%
a25
 
1.9%
r18
 
1.4%
y17
 
1.3%
l15
 
1.1%
Other values (16)89
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1313
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n501
38.2%
o485
36.9%
e55
 
4.2%
i51
 
3.9%
p29
 
2.2%
s28
 
2.1%
a25
 
1.9%
r18
 
1.4%
y17
 
1.3%
l15
 
1.1%
Other values (16)89
 
6.8%

Gastrointestinal Disease
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
511 
True
 
11
ValueCountFrequency (%)
False511
97.9%
True11
 
2.1%
2025-12-18T11:46:42.677767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Type of Gastrointestinal Disease
Categorical

High correlation  Imbalance 

Distinct6
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
no
511 
GERD
 
5
crohns' disease
 
2
crohn`s disease
 
2
PUD
 
1

Length

Max length15
Median length2
Mean length2.1321839
Min length2

Characters and Unicode

Total characters1113
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no511
97.9%
GERD5
 
1.0%
crohns' disease2
 
0.4%
crohn`s disease2
 
0.4%
PUD1
 
0.2%
PUD-GERD1
 
0.2%

Length

2025-12-18T11:46:42.774832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T11:46:42.855772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no511
97.1%
gerd5
 
1.0%
disease4
 
0.8%
crohns2
 
0.4%
crohn`s2
 
0.4%
pud1
 
0.2%
pud-gerd1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n515
46.3%
o515
46.3%
s12
 
1.1%
D8
 
0.7%
e8
 
0.7%
G6
 
0.5%
E6
 
0.5%
R6
 
0.5%
r4
 
0.4%
h4
 
0.4%
Other values (10)29
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)1113
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n515
46.3%
o515
46.3%
s12
 
1.1%
D8
 
0.7%
e8
 
0.7%
G6
 
0.5%
E6
 
0.5%
R6
 
0.5%
r4
 
0.4%
h4
 
0.4%
Other values (10)29
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1113
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n515
46.3%
o515
46.3%
s12
 
1.1%
D8
 
0.7%
e8
 
0.7%
G6
 
0.5%
E6
 
0.5%
R6
 
0.5%
r4
 
0.4%
h4
 
0.4%
Other values (10)29
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1113
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n515
46.3%
o515
46.3%
s12
 
1.1%
D8
 
0.7%
e8
 
0.7%
G6
 
0.5%
E6
 
0.5%
R6
 
0.5%
r4
 
0.4%
h4
 
0.4%
Other values (10)29
 
2.6%

Endocrine Disease
Unsupported

Rejected  Unsupported 

Missing1
Missing (%)0.2%
Memory size4.2 KiB

Type of Endocrine Disease
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
0
490 
hypothyroidism
 
27
hyperthyroidism
 
3
hypothyroidism-hashimoto`s disease
 
1
hypothyrodism
 
1

Length

Max length34
Median length1
Mean length1.8390805
Min length1

Characters and Unicode

Total characters960
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st row0
2nd row0
3rd row0
4th rowhyperthyroidism
5th row0

Common Values

ValueCountFrequency (%)
0490
93.9%
hypothyroidism27
 
5.2%
hyperthyroidism3
 
0.6%
hypothyroidism-hashimoto`s disease1
 
0.2%
hypothyrodism1
 
0.2%

Length

2025-12-18T11:46:42.964983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T11:46:43.054124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0490
93.7%
hypothyroidism27
 
5.2%
hyperthyroidism3
 
0.6%
hypothyroidism-hashimoto`s1
 
0.2%
disease1
 
0.2%
hypothyrodism1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0490
51.0%
h66
 
6.9%
i65
 
6.8%
y64
 
6.7%
o63
 
6.6%
s36
 
3.8%
r35
 
3.6%
t33
 
3.4%
m33
 
3.4%
d33
 
3.4%
Other values (6)42
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)960
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0490
51.0%
h66
 
6.9%
i65
 
6.8%
y64
 
6.7%
o63
 
6.6%
s36
 
3.8%
r35
 
3.6%
t33
 
3.4%
m33
 
3.4%
d33
 
3.4%
Other values (6)42
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)960
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0490
51.0%
h66
 
6.9%
i65
 
6.8%
y64
 
6.7%
o63
 
6.6%
s36
 
3.8%
r35
 
3.6%
t33
 
3.4%
m33
 
3.4%
d33
 
3.4%
Other values (6)42
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)960
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0490
51.0%
h66
 
6.9%
i65
 
6.8%
y64
 
6.7%
o63
 
6.6%
s36
 
3.8%
r35
 
3.6%
t33
 
3.4%
m33
 
3.4%
d33
 
3.4%
Other values (6)42
 
4.4%

Cancer
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size4.2 KiB

Type of cancer
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size4.2 KiB

Other_History
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size4.2 KiB

Current Medication
Boolean

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
299 
True
223 
ValueCountFrequency (%)
False299
57.3%
True223
42.7%
2025-12-18T11:46:43.122328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Antihypertensive
Boolean

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
430 
True
92 
ValueCountFrequency (%)
False430
82.4%
True92
 
17.6%
2025-12-18T11:46:43.169290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Antiplatelets
Boolean

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
422 
True
100 
ValueCountFrequency (%)
False422
80.8%
True100
 
19.2%
2025-12-18T11:46:43.216542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Anticoagulant
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
495 
True
 
27
ValueCountFrequency (%)
False495
94.8%
True27
 
5.2%
2025-12-18T11:46:43.270398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Antidiabetic
Boolean

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
453 
True
69 
ValueCountFrequency (%)
False453
86.8%
True69
 
13.2%
2025-12-18T11:46:43.316003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Thyroidal Medication
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
489 
True
 
33
ValueCountFrequency (%)
False489
93.7%
True33
 
6.3%
2025-12-18T11:46:43.361586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Antipsychotic
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
483 
True
 
39
ValueCountFrequency (%)
False483
92.5%
True39
 
7.5%
2025-12-18T11:46:43.403604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Betablocker
Boolean

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
430 
True
92 
ValueCountFrequency (%)
False430
82.4%
True92
 
17.6%
2025-12-18T11:46:43.451539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
461 
True
61 
ValueCountFrequency (%)
False461
88.3%
True61
 
11.7%
2025-12-18T11:46:43.499544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Diuratic
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
501 
True
 
21
ValueCountFrequency (%)
False501
96.0%
True21
 
4.0%
2025-12-18T11:46:43.550149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

OtherMedication
Categorical

Imbalance 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
no
512 
chemotherapy
 
9
immunsuppressive
 
1

Length

Max length16
Median length2
Mean length2.1992337
Min length2

Characters and Unicode

Total characters1148
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no512
98.1%
chemotherapy9
 
1.7%
immunsuppressive1
 
0.2%

Length

2025-12-18T11:46:43.632752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T11:46:43.703590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no512
98.1%
chemotherapy9
 
1.7%
immunsuppressive1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
o521
45.4%
n513
44.7%
e20
 
1.7%
h18
 
1.6%
m11
 
1.0%
p11
 
1.0%
r10
 
0.9%
t9
 
0.8%
c9
 
0.8%
a9
 
0.8%
Other values (5)17
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)1148
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o521
45.4%
n513
44.7%
e20
 
1.7%
h18
 
1.6%
m11
 
1.0%
p11
 
1.0%
r10
 
0.9%
t9
 
0.8%
c9
 
0.8%
a9
 
0.8%
Other values (5)17
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1148
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o521
45.4%
n513
44.7%
e20
 
1.7%
h18
 
1.6%
m11
 
1.0%
p11
 
1.0%
r10
 
0.9%
t9
 
0.8%
c9
 
0.8%
a9
 
0.8%
Other values (5)17
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1148
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o521
45.4%
n513
44.7%
e20
 
1.7%
h18
 
1.6%
m11
 
1.0%
p11
 
1.0%
r10
 
0.9%
t9
 
0.8%
c9
 
0.8%
a9
 
0.8%
Other values (5)17
 
1.5%
Distinct379
Distinct (%)72.6%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2025-12-18T11:46:44.014121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length292
Median length137
Mean length41.461686
Min length3

Characters and Unicode

Total characters21643
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique326 ?
Unique (%)62.5%

Sample

1st rowleft severe hydronephrosis-left pyeloplasty
2nd rowintestinal perforation-aortic dissection-ruptured abdominal aneurysm-urgent repair of abdominal aortic dissection was done on 4.5.2025 by Dr.Mohammad Saab
3rd rowprevious blast injury-infected bedsore-pneumonia-seizure-aspiration-septic shock on yes4.3.2025-wound culture on yes3.3.2025: heavy proteus mirabilis ESBL-tissue culture on yes2.4.2025:heavy klebsiella oxytoca CRE-heavy providence rettgri ESBL-blood culture on 4.4.2025:staphylococcus capitis
4th rowblast injury-amputation of left lower leg-spleen rupture-right leg fracture-eye injury-wound culture on yes.4.2025: few staphylococcus epidermidis, few staphylococcus warneri and few staphylococcus aureus methicillin resistant
5th rowaltered LOC-obstructive hydrocephaly-craniopharyngioma
ValueCountFrequency (%)
left97
 
4.2%
right76
 
3.3%
of57
 
2.4%
on56
 
2.4%
fracture45
 
1.9%
and43
 
1.8%
culture38
 
1.6%
hernia30
 
1.3%
cyst25
 
1.1%
fall22
 
0.9%
Other values (863)1839
79.0%
2025-12-18T11:46:44.497515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1806
 
8.3%
e1777
 
8.2%
t1598
 
7.4%
a1442
 
6.7%
i1436
 
6.6%
o1375
 
6.4%
r1247
 
5.8%
l1120
 
5.2%
n1093
 
5.1%
s1080
 
5.0%
Other values (58)7669
35.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)21643
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1806
 
8.3%
e1777
 
8.2%
t1598
 
7.4%
a1442
 
6.7%
i1436
 
6.6%
o1375
 
6.4%
r1247
 
5.8%
l1120
 
5.2%
n1093
 
5.1%
s1080
 
5.0%
Other values (58)7669
35.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)21643
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1806
 
8.3%
e1777
 
8.2%
t1598
 
7.4%
a1442
 
6.7%
i1436
 
6.6%
o1375
 
6.4%
r1247
 
5.8%
l1120
 
5.2%
n1093
 
5.1%
s1080
 
5.0%
Other values (58)7669
35.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)21643
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1806
 
8.3%
e1777
 
8.2%
t1598
 
7.4%
a1442
 
6.7%
i1436
 
6.6%
o1375
 
6.4%
r1247
 
5.8%
l1120
 
5.2%
n1093
 
5.1%
s1080
 
5.0%
Other values (58)7669
35.4%

ICD10
Text

Missing 

Distinct181
Distinct (%)59.0%
Missing215
Missing (%)41.2%
Memory size4.2 KiB
2025-12-18T11:46:44.822469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length23
Median length17
Mean length5.2801303
Min length3

Characters and Unicode

Total characters1621
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique133 ?
Unique (%)43.3%

Sample

1st rowN30
2nd rowJ69.9-L89.9
3rd rowT14.8-S36.0-Y89.1-Y83.5
4th rowN18.6-A41.9-R65.2
5th rowN18.6-A41.9-R65.2
ValueCountFrequency (%)
k8019
 
6.2%
k409
 
2.9%
n209
 
2.9%
c677
 
2.3%
z47.06
 
2.0%
j35.1-h65.16
 
2.0%
j34.2-j34.35
 
1.6%
n635
 
1.6%
k424
 
1.3%
n304
 
1.3%
Other values (171)233
75.9%
2025-12-18T11:46:45.277435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
.226
13.9%
0152
 
9.4%
3125
 
7.7%
1118
 
7.3%
2113
 
7.0%
497
 
6.0%
595
 
5.9%
680
 
4.9%
971
 
4.4%
871
 
4.4%
Other values (20)473
29.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)1621
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.226
13.9%
0152
 
9.4%
3125
 
7.7%
1118
 
7.3%
2113
 
7.0%
497
 
6.0%
595
 
5.9%
680
 
4.9%
971
 
4.4%
871
 
4.4%
Other values (20)473
29.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1621
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.226
13.9%
0152
 
9.4%
3125
 
7.7%
1118
 
7.3%
2113
 
7.0%
497
 
6.0%
595
 
5.9%
680
 
4.9%
971
 
4.4%
871
 
4.4%
Other values (20)473
29.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1621
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.226
13.9%
0152
 
9.4%
3125
 
7.7%
1118
 
7.3%
2113
 
7.0%
497
 
6.0%
595
 
5.9%
680
 
4.9%
971
 
4.4%
871
 
4.4%
Other values (20)473
29.2%

ER Admission Before Surgery
Boolean

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
420 
True
102 
ValueCountFrequency (%)
False420
80.5%
True102
 
19.5%
2025-12-18T11:46:45.362660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct74
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2025-12-18T11:46:45.557140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length39
Median length2
Mean length4.9425287
Min length2

Characters and Unicode

Total characters2580
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57 ?
Unique (%)10.9%

Sample

1st rowno
2nd rowaneurysm-aortic dissection
3rd rowepilepsy
4th rowblast injury
5th rowno
ValueCountFrequency (%)
no420
64.0%
fracture26
 
4.0%
right19
 
2.9%
left14
 
2.1%
hip11
 
1.7%
cyst8
 
1.2%
distal7
 
1.1%
appendicitis7
 
1.1%
icb5
 
0.8%
hernia5
 
0.8%
Other values (91)134
 
20.4%
2025-12-18T11:46:45.928147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n500
19.4%
o464
18.0%
i165
 
6.4%
t159
 
6.2%
r147
 
5.7%
a143
 
5.5%
e140
 
5.4%
134
 
5.2%
s99
 
3.8%
c82
 
3.2%
Other values (36)547
21.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)2580
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n500
19.4%
o464
18.0%
i165
 
6.4%
t159
 
6.2%
r147
 
5.7%
a143
 
5.5%
e140
 
5.4%
134
 
5.2%
s99
 
3.8%
c82
 
3.2%
Other values (36)547
21.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2580
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n500
19.4%
o464
18.0%
i165
 
6.4%
t159
 
6.2%
r147
 
5.7%
a143
 
5.5%
e140
 
5.4%
134
 
5.2%
s99
 
3.8%
c82
 
3.2%
Other values (36)547
21.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2580
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n500
19.4%
o464
18.0%
i165
 
6.4%
t159
 
6.2%
r147
 
5.7%
a143
 
5.5%
e140
 
5.4%
134
 
5.2%
s99
 
3.8%
c82
 
3.2%
Other values (36)547
21.2%

Radiology
Boolean

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
423 
True
99 
ValueCountFrequency (%)
False423
81.0%
True99
 
19.0%
2025-12-18T11:46:46.008545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Type of medical Imaging
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
no
423 
CT
 
39
yes
 
26
Echo
 
18
MRI
 
16

Length

Max length4
Median length2
Mean length2.1494253
Min length2

Characters and Unicode

Total characters1122
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowCT
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no423
81.0%
CT39
 
7.5%
yes26
 
5.0%
Echo18
 
3.4%
MRI16
 
3.1%

Length

2025-12-18T11:46:46.093709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T11:46:46.174399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no423
81.0%
ct39
 
7.5%
yes26
 
5.0%
echo18
 
3.4%
mri16
 
3.1%

Most occurring characters

ValueCountFrequency (%)
o441
39.3%
n423
37.7%
C39
 
3.5%
T39
 
3.5%
y26
 
2.3%
e26
 
2.3%
s26
 
2.3%
E18
 
1.6%
c18
 
1.6%
h18
 
1.6%
Other values (3)48
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1122
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o441
39.3%
n423
37.7%
C39
 
3.5%
T39
 
3.5%
y26
 
2.3%
e26
 
2.3%
s26
 
2.3%
E18
 
1.6%
c18
 
1.6%
h18
 
1.6%
Other values (3)48
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1122
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o441
39.3%
n423
37.7%
C39
 
3.5%
T39
 
3.5%
y26
 
2.3%
e26
 
2.3%
s26
 
2.3%
E18
 
1.6%
c18
 
1.6%
h18
 
1.6%
Other values (3)48
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1122
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o441
39.3%
n423
37.7%
C39
 
3.5%
T39
 
3.5%
y26
 
2.3%
e26
 
2.3%
s26
 
2.3%
E18
 
1.6%
c18
 
1.6%
h18
 
1.6%
Other values (3)48
 
4.3%
Distinct89
Distinct (%)17.1%
Missing1
Missing (%)0.2%
Memory size4.2 KiB
2025-12-18T11:46:46.361251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length167
Median length1
Mean length7.6506718
Min length1

Characters and Unicode

Total characters3986
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80 ?
Unique (%)15.4%

Sample

1st row0
2nd rowruptured aneurysm-hematoma
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0422
46.8%
fracture30
 
3.3%
right27
 
3.0%
left20
 
2.2%
the16
 
1.8%
distal12
 
1.3%
and11
 
1.2%
in8
 
0.9%
of7
 
0.8%
at7
 
0.8%
Other values (231)341
37.8%
2025-12-18T11:46:46.714211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0425
 
10.7%
380
 
9.5%
e321
 
8.1%
t301
 
7.6%
a284
 
7.1%
r260
 
6.5%
i255
 
6.4%
s187
 
4.7%
l179
 
4.5%
o171
 
4.3%
Other values (41)1223
30.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)3986
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0425
 
10.7%
380
 
9.5%
e321
 
8.1%
t301
 
7.6%
a284
 
7.1%
r260
 
6.5%
i255
 
6.4%
s187
 
4.7%
l179
 
4.5%
o171
 
4.3%
Other values (41)1223
30.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3986
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0425
 
10.7%
380
 
9.5%
e321
 
8.1%
t301
 
7.6%
a284
 
7.1%
r260
 
6.5%
i255
 
6.4%
s187
 
4.7%
l179
 
4.5%
o171
 
4.3%
Other values (41)1223
30.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3986
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0425
 
10.7%
380
 
9.5%
e321
 
8.1%
t301
 
7.6%
a284
 
7.1%
r260
 
6.5%
i255
 
6.4%
s187
 
4.7%
l179
 
4.5%
o171
 
4.3%
Other values (41)1223
30.7%

Pre-BUN
Real number (ℝ)

High correlation  Missing 

Distinct43
Distinct (%)13.4%
Missing200
Missing (%)38.3%
Infinite0
Infinite (%)0.0%
Mean19.379814
Minimum5
Maximum170
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2025-12-18T11:46:46.829896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile9
Q112
median15
Q319
95-th percentile47
Maximum170
Range165
Interquartile range (IQR)7

Descriptive statistics

Standard deviation18.345341
Coefficient of variation (CV)0.94662111
Kurtosis34.274002
Mean19.379814
Median Absolute Deviation (MAD)3
Skewness5.2723949
Sum6240.3
Variance336.55152
MonotonicityNot monotonic
2025-12-18T11:46:46.969866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1332
 
6.1%
1429
 
5.6%
1524
 
4.6%
1224
 
4.6%
1623
 
4.4%
1123
 
4.4%
1722
 
4.2%
916
 
3.1%
2115
 
2.9%
1914
 
2.7%
Other values (33)100
19.2%
(Missing)200
38.3%
ValueCountFrequency (%)
52
 
0.4%
73
 
0.6%
88
 
1.5%
916
3.1%
1011
 
2.1%
1123
4.4%
1224
4.6%
1332
6.1%
1429
5.6%
1524
4.6%
ValueCountFrequency (%)
1702
0.4%
1212
0.4%
961
0.2%
802
0.4%
771
0.2%
591
0.2%
582
0.4%
562
0.4%
511
0.2%
482
0.4%

BUN day 1 post surgery
Real number (ℝ)

High correlation  Missing 

Distinct33
Distinct (%)54.1%
Missing461
Missing (%)88.3%
Infinite0
Infinite (%)0.0%
Mean21.113115
Minimum4
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2025-12-18T11:46:47.085382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6
Q111
median15
Q325
95-th percentile51
Maximum93
Range89
Interquartile range (IQR)14

Descriptive statistics

Standard deviation17.386311
Coefficient of variation (CV)0.82348396
Kurtosis6.2023715
Mean21.113115
Median Absolute Deviation (MAD)5
Skewness2.3111527
Sum1287.9
Variance302.28383
MonotonicityNot monotonic
2025-12-18T11:46:47.192062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
145
 
1.0%
105
 
1.0%
134
 
0.8%
154
 
0.8%
173
 
0.6%
163
 
0.6%
123
 
0.6%
212
 
0.4%
262
 
0.4%
42
 
0.4%
Other values (23)28
 
5.4%
(Missing)461
88.3%
ValueCountFrequency (%)
42
 
0.4%
51
 
0.2%
62
 
0.4%
6.91
 
0.2%
71
 
0.2%
81
 
0.2%
91
 
0.2%
105
1.0%
112
 
0.4%
123
0.6%
ValueCountFrequency (%)
931
0.2%
831
0.2%
601
0.2%
511
0.2%
501
0.2%
422
0.4%
411
0.2%
361
0.2%
331
0.2%
321
0.2%

BUN before Discharge
Real number (ℝ)

High correlation  Missing 

Distinct26
Distinct (%)78.8%
Missing489
Missing (%)93.7%
Infinite0
Infinite (%)0.0%
Mean23.515152
Minimum3
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2025-12-18T11:46:47.295126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5.2
Q114
median20
Q327
95-th percentile47.4
Maximum70
Range67
Interquartile range (IQR)13

Descriptive statistics

Standard deviation14.981491
Coefficient of variation (CV)0.63709949
Kurtosis1.5714143
Mean23.515152
Median Absolute Deviation (MAD)7
Skewness1.1337682
Sum776
Variance224.44508
MonotonicityNot monotonic
2025-12-18T11:46:47.398567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
273
 
0.6%
452
 
0.4%
232
 
0.4%
192
 
0.4%
402
 
0.4%
162
 
0.4%
131
 
0.2%
151
 
0.2%
381
 
0.2%
111
 
0.2%
Other values (16)16
 
3.1%
(Missing)489
93.7%
ValueCountFrequency (%)
31
0.2%
41
0.2%
61
0.2%
71
0.2%
81
0.2%
91
0.2%
111
0.2%
131
0.2%
141
0.2%
151
0.2%
ValueCountFrequency (%)
701
 
0.2%
511
 
0.2%
452
0.4%
402
0.4%
381
 
0.2%
301
 
0.2%
273
0.6%
261
 
0.2%
251
 
0.2%
241
 
0.2%

Pre-Creatinine
Real number (ℝ)

High correlation  Missing 

Distinct121
Distinct (%)29.1%
Missing106
Missing (%)20.3%
Infinite0
Infinite (%)0.0%
Mean1.1283654
Minimum0.27
Maximum13.31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2025-12-18T11:46:47.518222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.27
5-th percentile0.48
Q10.65
median0.8
Q30.97
95-th percentile2.7475
Maximum13.31
Range13.04
Interquartile range (IQR)0.32

Descriptive statistics

Standard deviation1.4800131
Coefficient of variation (CV)1.3116435
Kurtosis29.119399
Mean1.1283654
Median Absolute Deviation (MAD)0.15
Skewness5.110765
Sum469.4
Variance2.1904388
MonotonicityNot monotonic
2025-12-18T11:46:47.654679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.812
 
2.3%
0.7312
 
2.3%
0.8210
 
1.9%
0.8410
 
1.9%
0.749
 
1.7%
0.659
 
1.7%
0.679
 
1.7%
0.689
 
1.7%
0.588
 
1.5%
0.728
 
1.5%
Other values (111)320
61.3%
(Missing)106
 
20.3%
ValueCountFrequency (%)
0.271
 
0.2%
0.321
 
0.2%
0.331
 
0.2%
0.351
 
0.2%
0.361
 
0.2%
0.381
 
0.2%
0.393
0.6%
0.43
0.6%
0.411
 
0.2%
0.423
0.6%
ValueCountFrequency (%)
13.311
0.2%
11.621
0.2%
10.552
0.4%
8.671
0.2%
7.671
0.2%
7.531
0.2%
7.221
0.2%
6.91
0.2%
6.891
0.2%
6.611
0.2%

Creatinine_D1
Real number (ℝ)

High correlation  Missing 

Distinct67
Distinct (%)81.7%
Missing440
Missing (%)84.3%
Infinite0
Infinite (%)0.0%
Mean1.5582927
Minimum0.26
Maximum11.82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2025-12-18T11:46:47.790942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.26
5-th percentile0.43
Q10.6325
median0.825
Q31.35
95-th percentile5.5915
Maximum11.82
Range11.56
Interquartile range (IQR)0.7175

Descriptive statistics

Standard deviation1.9653215
Coefficient of variation (CV)1.2612017
Kurtosis10.905262
Mean1.5582927
Median Absolute Deviation (MAD)0.31
Skewness3.1199558
Sum127.78
Variance3.8624884
MonotonicityNot monotonic
2025-12-18T11:46:47.937156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.684
 
0.8%
0.433
 
0.6%
0.823
 
0.6%
0.82
 
0.4%
1.132
 
0.4%
0.512
 
0.4%
0.812
 
0.4%
0.672
 
0.4%
0.612
 
0.4%
12
 
0.4%
Other values (57)58
 
11.1%
(Missing)440
84.3%
ValueCountFrequency (%)
0.261
 
0.2%
0.381
 
0.2%
0.411
 
0.2%
0.421
 
0.2%
0.433
0.6%
0.451
 
0.2%
0.481
 
0.2%
0.491
 
0.2%
0.51
 
0.2%
0.512
0.4%
ValueCountFrequency (%)
11.821
0.2%
8.041
0.2%
7.751
0.2%
6.881
0.2%
5.641
0.2%
4.671
0.2%
4.61
0.2%
4.51
0.2%
3.541
0.2%
3.531
0.2%

Creatinine before Discharge
Real number (ℝ)

High correlation  Missing 

Distinct34
Distinct (%)72.3%
Missing475
Missing (%)91.0%
Infinite0
Infinite (%)0.0%
Mean1.1278723
Minimum0.28
Maximum6.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2025-12-18T11:46:48.052805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.28
5-th percentile0.409
Q10.62
median0.86
Q31.31
95-th percentile2.107
Maximum6.87
Range6.59
Interquartile range (IQR)0.69

Descriptive statistics

Standard deviation1.0114646
Coefficient of variation (CV)0.89678995
Kurtosis22.876876
Mean1.1278723
Median Absolute Deviation (MAD)0.28
Skewness4.2461777
Sum53.01
Variance1.0230606
MonotonicityNot monotonic
2025-12-18T11:46:48.168642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0.624
 
0.8%
1.543
 
0.6%
0.63
 
0.6%
0.992
 
0.4%
0.582
 
0.4%
0.682
 
0.4%
0.632
 
0.4%
1.962
 
0.4%
0.862
 
0.4%
1.551
 
0.2%
Other values (24)24
 
4.6%
(Missing)475
91.0%
ValueCountFrequency (%)
0.281
 
0.2%
0.31
 
0.2%
0.41
 
0.2%
0.431
 
0.2%
0.541
 
0.2%
0.582
0.4%
0.63
0.6%
0.611
 
0.2%
0.624
0.8%
0.632
0.4%
ValueCountFrequency (%)
6.871
 
0.2%
2.91
 
0.2%
2.171
 
0.2%
1.962
0.4%
1.781
 
0.2%
1.551
 
0.2%
1.543
0.6%
1.391
 
0.2%
1.371
 
0.2%
1.251
 
0.2%

Pre Na
Real number (ℝ)

High correlation  Missing 

Distinct22
Distinct (%)6.6%
Missing187
Missing (%)35.8%
Infinite0
Infinite (%)0.0%
Mean138.88358
Minimum121
Maximum153
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2025-12-18T11:46:48.275692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum121
5-th percentile134
Q1138
median139
Q3141
95-th percentile142.3
Maximum153
Range32
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.0897065
Coefficient of variation (CV)0.022246737
Kurtosis7.3009521
Mean138.88358
Median Absolute Deviation (MAD)1
Skewness-1.1413619
Sum46526
Variance9.5462865
MonotonicityNot monotonic
2025-12-18T11:46:48.371221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
13966
 
12.6%
14059
 
11.3%
13846
 
8.8%
14144
 
8.4%
13728
 
5.4%
14227
 
5.2%
13413
 
2.5%
13613
 
2.5%
14311
 
2.1%
1359
 
1.7%
Other values (12)19
 
3.6%
(Missing)187
35.8%
ValueCountFrequency (%)
1211
 
0.2%
1231
 
0.2%
1261
 
0.2%
1281
 
0.2%
1301
 
0.2%
1325
 
1.0%
1333
 
0.6%
13413
2.5%
1359
1.7%
13613
2.5%
ValueCountFrequency (%)
1531
 
0.2%
1491
 
0.2%
1481
 
0.2%
1471
 
0.2%
1442
 
0.4%
14311
 
2.1%
14227
5.2%
14144
8.4%
14059
11.3%
13966
12.6%

Na day 1 post surgery
Real number (ℝ)

High correlation  Missing 

Distinct18
Distinct (%)21.2%
Missing437
Missing (%)83.7%
Infinite0
Infinite (%)0.0%
Mean138.96471
Minimum129
Maximum151
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2025-12-18T11:46:48.466168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum129
5-th percentile133.2
Q1137
median139
Q3141
95-th percentile144.8
Maximum151
Range22
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.6561985
Coefficient of variation (CV)0.026310267
Kurtosis2.0955127
Mean138.96471
Median Absolute Deviation (MAD)2
Skewness0.48539179
Sum11812
Variance13.367787
MonotonicityNot monotonic
2025-12-18T11:46:48.557311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
13914
 
2.7%
14211
 
2.1%
13811
 
2.1%
1418
 
1.5%
1378
 
1.5%
1408
 
1.5%
1356
 
1.1%
1365
 
1.0%
1512
 
0.4%
1342
 
0.4%
Other values (8)10
 
1.9%
(Missing)437
83.7%
ValueCountFrequency (%)
1291
 
0.2%
1311
 
0.2%
1321
 
0.2%
1332
 
0.4%
1342
 
0.4%
1356
1.1%
1365
 
1.0%
1378
1.5%
13811
2.1%
13914
2.7%
ValueCountFrequency (%)
1512
 
0.4%
1471
 
0.2%
1452
 
0.4%
1441
 
0.2%
1431
 
0.2%
14211
2.1%
1418
1.5%
1408
1.5%
13914
2.7%
13811
2.1%

Na before Discharge
Real number (ℝ)

High correlation  Missing 

Distinct13
Distinct (%)23.2%
Missing466
Missing (%)89.3%
Infinite0
Infinite (%)0.0%
Mean138.05357
Minimum128
Maximum147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2025-12-18T11:46:48.642630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum128
5-th percentile132.5
Q1135.75
median139
Q3140
95-th percentile143
Maximum147
Range19
Interquartile range (IQR)4.25

Descriptive statistics

Standard deviation3.7727234
Coefficient of variation (CV)0.027327966
Kurtosis1.2004993
Mean138.05357
Median Absolute Deviation (MAD)2
Skewness-0.67305181
Sum7731
Variance14.233442
MonotonicityNot monotonic
2025-12-18T11:46:48.736606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
13913
 
2.5%
1346
 
1.1%
1406
 
1.1%
1385
 
1.0%
1425
 
1.0%
1355
 
1.0%
1433
 
0.6%
1283
 
0.6%
1373
 
0.6%
1363
 
0.6%
Other values (3)4
 
0.8%
(Missing)466
89.3%
ValueCountFrequency (%)
1283
 
0.6%
1346
1.1%
1355
 
1.0%
1363
 
0.6%
1373
 
0.6%
1385
 
1.0%
13913
2.5%
1406
1.1%
1412
 
0.4%
1425
 
1.0%
ValueCountFrequency (%)
1471
 
0.2%
1441
 
0.2%
1433
 
0.6%
1425
 
1.0%
1412
 
0.4%
1406
1.1%
13913
2.5%
1385
 
1.0%
1373
 
0.6%
1363
 
0.6%

Pre HB
Real number (ℝ)

High correlation  Missing 

Distinct96
Distinct (%)18.8%
Missing10
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean13.378125
Minimum5
Maximum18.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2025-12-18T11:46:48.859392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile9.655
Q111.9
median13.4
Q315
95-th percentile16.7
Maximum18.6
Range13.6
Interquartile range (IQR)3.1

Descriptive statistics

Standard deviation2.2070404
Coefficient of variation (CV)0.16497382
Kurtosis0.29556866
Mean13.378125
Median Absolute Deviation (MAD)1.5
Skewness-0.39162879
Sum6849.6
Variance4.8710274
MonotonicityNot monotonic
2025-12-18T11:46:49.010475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.614
 
2.7%
12.913
 
2.5%
13.713
 
2.5%
11.613
 
2.5%
15.112
 
2.3%
14.112
 
2.3%
13.312
 
2.3%
13.111
 
2.1%
12.711
 
2.1%
12.611
 
2.1%
Other values (86)390
74.7%
ValueCountFrequency (%)
51
 
0.2%
5.51
 
0.2%
7.22
0.4%
7.43
0.6%
7.71
 
0.2%
7.91
 
0.2%
81
 
0.2%
8.41
 
0.2%
8.72
0.4%
8.81
 
0.2%
ValueCountFrequency (%)
18.62
 
0.4%
181
 
0.2%
17.91
 
0.2%
17.63
0.6%
17.51
 
0.2%
17.42
 
0.4%
17.23
0.6%
17.12
 
0.4%
175
1.0%
16.92
 
0.4%

HB day 1 post surgery
Real number (ℝ)

High correlation  Missing 

Distinct62
Distinct (%)51.2%
Missing401
Missing (%)76.8%
Infinite0
Infinite (%)0.0%
Mean11.094545
Minimum7
Maximum15.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2025-12-18T11:46:49.140795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.7
Q19.5
median11.2
Q312.5
95-th percentile14.1
Maximum15.9
Range8.9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9996
Coefficient of variation (CV)0.18023271
Kurtosis-0.72551432
Mean11.094545
Median Absolute Deviation (MAD)1.5
Skewness-0.065212529
Sum1342.44
Variance3.9984
MonotonicityNot monotonic
2025-12-18T11:46:49.268808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.55
 
1.0%
10.45
 
1.0%
12.15
 
1.0%
11.25
 
1.0%
12.24
 
0.8%
11.94
 
0.8%
8.94
 
0.8%
11.83
 
0.6%
12.73
 
0.6%
12.93
 
0.6%
Other values (52)80
 
15.3%
(Missing)401
76.8%
ValueCountFrequency (%)
71
0.2%
7.11
0.2%
7.21
0.2%
7.52
0.4%
7.61
0.2%
7.71
0.2%
7.82
0.4%
8.22
0.4%
8.51
0.2%
8.62
0.4%
ValueCountFrequency (%)
15.91
0.2%
15.21
0.2%
14.81
0.2%
14.42
0.4%
14.12
0.4%
142
0.4%
13.91
0.2%
13.82
0.4%
13.72
0.4%
13.41
0.2%

HB before discharge
Real number (ℝ)

High correlation  Missing 

Distinct41
Distinct (%)60.3%
Missing454
Missing (%)87.0%
Infinite0
Infinite (%)0.0%
Mean10.879412
Minimum7.4
Maximum15.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2025-12-18T11:46:49.387230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7.4
5-th percentile7.9
Q19.5
median10.6
Q312.3
95-th percentile13.895
Maximum15.8
Range8.4
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation1.880819
Coefficient of variation (CV)0.17287874
Kurtosis-0.31282244
Mean10.879412
Median Absolute Deviation (MAD)1.3
Skewness0.30503922
Sum739.8
Variance3.5374802
MonotonicityNot monotonic
2025-12-18T11:46:49.503987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
9.34
 
0.8%
10.23
 
0.6%
12.43
 
0.6%
9.63
 
0.6%
9.73
 
0.6%
10.63
 
0.6%
11.33
 
0.6%
12.23
 
0.6%
12.92
 
0.4%
10.42
 
0.4%
Other values (31)39
 
7.5%
(Missing)454
87.0%
ValueCountFrequency (%)
7.41
 
0.2%
7.72
0.4%
7.92
0.4%
8.21
 
0.2%
8.41
 
0.2%
8.61
 
0.2%
8.71
 
0.2%
91
 
0.2%
9.34
0.8%
9.42
0.4%
ValueCountFrequency (%)
15.81
0.2%
15.21
0.2%
14.51
0.2%
141
0.2%
13.71
0.2%
13.41
0.2%
13.21
0.2%
12.92
0.4%
12.81
0.2%
12.72
0.4%

Pre Platelet
Real number (ℝ)

High correlation  Missing 

Distinct250
Distinct (%)48.9%
Missing11
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean281.3953
Minimum33
Maximum990
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2025-12-18T11:46:49.631160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile159
Q1218
median275
Q3329
95-th percentile433
Maximum990
Range957
Interquartile range (IQR)111

Descriptive statistics

Standard deviation91.805551
Coefficient of variation (CV)0.32625118
Kurtosis7.5056565
Mean281.3953
Median Absolute Deviation (MAD)56
Skewness1.5297214
Sum143793
Variance8428.2591
MonotonicityNot monotonic
2025-12-18T11:46:49.764672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2726
 
1.1%
2736
 
1.1%
3326
 
1.1%
3025
 
1.0%
2565
 
1.0%
2245
 
1.0%
2275
 
1.0%
3075
 
1.0%
2705
 
1.0%
2795
 
1.0%
Other values (240)458
87.7%
(Missing)11
 
2.1%
ValueCountFrequency (%)
331
0.2%
1051
0.2%
1141
0.2%
1181
0.2%
1201
0.2%
1271
0.2%
1321
0.2%
1392
0.4%
1401
0.2%
1411
0.2%
ValueCountFrequency (%)
9901
0.2%
6871
0.2%
5952
0.4%
5821
0.2%
5521
0.2%
5431
0.2%
5411
0.2%
5131
0.2%
5071
0.2%
5041
0.2%

Platelet day 1 post surgery
Real number (ℝ)

High correlation  Missing 

Distinct99
Distinct (%)81.8%
Missing401
Missing (%)76.8%
Infinite0
Infinite (%)0.0%
Mean259.80165
Minimum98
Maximum519
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2025-12-18T11:46:49.892158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum98
5-th percentile128
Q1191
median245
Q3308
95-th percentile448
Maximum519
Range421
Interquartile range (IQR)117

Descriptive statistics

Standard deviation95.8708
Coefficient of variation (CV)0.36901536
Kurtosis0.24691046
Mean259.80165
Median Absolute Deviation (MAD)60
Skewness0.76180761
Sum31436
Variance9191.2103
MonotonicityNot monotonic
2025-12-18T11:46:50.033587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1962
 
0.4%
2612
 
0.4%
2172
 
0.4%
1262
 
0.4%
2972
 
0.4%
1712
 
0.4%
3322
 
0.4%
2452
 
0.4%
2422
 
0.4%
2162
 
0.4%
Other values (89)101
 
19.3%
(Missing)401
76.8%
ValueCountFrequency (%)
981
0.2%
1091
0.2%
1201
0.2%
1221
0.2%
1262
0.4%
1281
0.2%
1291
0.2%
1311
0.2%
1371
0.2%
1411
0.2%
ValueCountFrequency (%)
5191
0.2%
5131
0.2%
5012
0.4%
4911
0.2%
4541
0.2%
4481
0.2%
4411
0.2%
4381
0.2%
4351
0.2%
4181
0.2%

Platelet befor eDischarge
Real number (ℝ)

High correlation  Missing 

Distinct57
Distinct (%)83.8%
Missing454
Missing (%)87.0%
Infinite0
Infinite (%)0.0%
Mean258.26471
Minimum46
Maximum497
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2025-12-18T11:46:50.159097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile114.85
Q1203.5
median235.5
Q3319.25
95-th percentile458
Maximum497
Range451
Interquartile range (IQR)115.75

Descriptive statistics

Standard deviation102.41569
Coefficient of variation (CV)0.39655318
Kurtosis0.039600958
Mean258.26471
Median Absolute Deviation (MAD)60.5
Skewness0.34326438
Sum17562
Variance10488.974
MonotonicityNot monotonic
2025-12-18T11:46:50.293646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
463
 
0.6%
2142
 
0.4%
1452
 
0.4%
1992
 
0.4%
2282
 
0.4%
2122
 
0.4%
3682
 
0.4%
3002
 
0.4%
2372
 
0.4%
4582
 
0.4%
Other values (47)47
 
9.0%
(Missing)454
87.0%
ValueCountFrequency (%)
463
0.6%
1041
 
0.2%
1351
 
0.2%
1371
 
0.2%
1452
0.4%
1471
 
0.2%
1561
 
0.2%
1721
 
0.2%
1741
 
0.2%
1771
 
0.2%
ValueCountFrequency (%)
4971
0.2%
4941
0.2%
4631
0.2%
4582
0.4%
4081
0.2%
4051
0.2%
3851
0.2%
3682
0.4%
3641
0.2%
3611
0.2%
Distinct30
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
Minimum2025-04-01 00:00:00
Maximum2025-04-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-12-18T11:46:50.407385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:50.523929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)

Code of surgeon
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size4.2 KiB

Surgery
Categorical

High correlation 

Distinct9
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
General surgery
169 
Orthopedic surgery
109 
Otolaryngic surgery
77 
Urologic surgery
66 
Neurosurgery
57 
Other values (4)
44 

Length

Max length34
Median length21
Mean length16.362069
Min length12

Characters and Unicode

Total characters8541
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrologic surgery
2nd rowGeneral surgery
3rd rowGeneral surgery
4th rowOrthopedic surgery
5th rowNeurosurgery

Common Values

ValueCountFrequency (%)
General surgery169
32.4%
Orthopedic surgery109
20.9%
Otolaryngic surgery77
14.8%
Urologic surgery66
 
12.6%
Neurosurgery57
 
10.9%
Vascular surgery25
 
4.8%
Gynecological surgery7
 
1.3%
Plastic and reconstruction surgery6
 
1.1%
Transplantation6
 
1.1%

Length

2025-12-18T11:46:50.630006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T11:46:50.723135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
surgery459
46.2%
general169
 
17.0%
orthopedic109
 
11.0%
otolaryngic77
 
7.8%
urologic66
 
6.6%
neurosurgery57
 
5.7%
vascular25
 
2.5%
gynecological7
 
0.7%
plastic6
 
0.6%
and6
 
0.6%
Other values (2)12
 
1.2%

Most occurring characters

ValueCountFrequency (%)
r1553
18.2%
e1033
12.1%
g666
 
7.8%
u604
 
7.1%
y600
 
7.0%
s559
 
6.5%
471
 
5.5%
o407
 
4.8%
l363
 
4.3%
a333
 
3.9%
Other values (14)1952
22.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)8541
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r1553
18.2%
e1033
12.1%
g666
 
7.8%
u604
 
7.1%
y600
 
7.0%
s559
 
6.5%
471
 
5.5%
o407
 
4.8%
l363
 
4.3%
a333
 
3.9%
Other values (14)1952
22.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8541
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r1553
18.2%
e1033
12.1%
g666
 
7.8%
u604
 
7.1%
y600
 
7.0%
s559
 
6.5%
471
 
5.5%
o407
 
4.8%
l363
 
4.3%
a333
 
3.9%
Other values (14)1952
22.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8541
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r1553
18.2%
e1033
12.1%
g666
 
7.8%
u604
 
7.1%
y600
 
7.0%
s559
 
6.5%
471
 
5.5%
o407
 
4.8%
l363
 
4.3%
a333
 
3.9%
Other values (14)1952
22.9%

Emergency Status of surgery
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
473 
True
49 
ValueCountFrequency (%)
False473
90.6%
True49
 
9.4%
2025-12-18T11:46:50.827777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct92
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2025-12-18T11:46:51.029834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length28
Median length22
Mean length14.534483
Min length3

Characters and Unicode

Total characters7587
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)5.4%

Sample

1st rowpyeloplasty
2nd rowcolectomy
3rd rowexcision of cyst-mass
4th rowamputation
5th rowُEVD
ValueCountFrequency (%)
surgery133
 
14.5%
of67
 
7.3%
excision58
 
6.3%
cyst-mass58
 
6.3%
lower35
 
3.8%
laminectomy30
 
3.3%
hernia30
 
3.3%
cholecystectomy26
 
2.8%
arm19
 
2.1%
adenoidectomy-tympanostomy18
 
2.0%
Other values (105)445
48.4%
2025-12-18T11:46:51.373821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e732
 
9.6%
o652
 
8.6%
s593
 
7.8%
r542
 
7.1%
y517
 
6.8%
t516
 
6.8%
m435
 
5.7%
c412
 
5.4%
i405
 
5.3%
397
 
5.2%
Other values (33)2386
31.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)7587
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e732
 
9.6%
o652
 
8.6%
s593
 
7.8%
r542
 
7.1%
y517
 
6.8%
t516
 
6.8%
m435
 
5.7%
c412
 
5.4%
i405
 
5.3%
397
 
5.2%
Other values (33)2386
31.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)7587
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e732
 
9.6%
o652
 
8.6%
s593
 
7.8%
r542
 
7.1%
y517
 
6.8%
t516
 
6.8%
m435
 
5.7%
c412
 
5.4%
i405
 
5.3%
397
 
5.2%
Other values (33)2386
31.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)7587
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e732
 
9.6%
o652
 
8.6%
s593
 
7.8%
r542
 
7.1%
y517
 
6.8%
t516
 
6.8%
m435
 
5.7%
c412
 
5.4%
i405
 
5.3%
397
 
5.2%
Other values (33)2386
31.4%
Distinct331
Distinct (%)63.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2025-12-18T11:46:51.831921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length175
Median length122
Mean length34.097701
Min length3

Characters and Unicode

Total characters17799
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique276 ?
Unique (%)52.9%

Sample

1st rowleft pyeloplasty
2nd rowperforation of right colon-right hemicolectomy-anastomosis-ileostomy
3rd rowexcision of necrotic tissue of bilateral feet
4th rowbilateral leg debridement-insertion of steinmenn pin right leg-amputation left foot-splenectomy-bleeding control-exploration of both eyes
5th rowEVD insertion in the right ventricle
ValueCountFrequency (%)
of145
 
6.2%
left125
 
5.4%
right118
 
5.1%
and79
 
3.4%
repair52
 
2.2%
excision45
 
1.9%
fixation37
 
1.6%
fracture36
 
1.5%
removal32
 
1.4%
hernia30
 
1.3%
Other values (605)1627
69.9%
2025-12-18T11:46:52.583277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1805
 
10.1%
e1444
 
8.1%
t1412
 
7.9%
i1315
 
7.4%
a1265
 
7.1%
o1224
 
6.9%
r1109
 
6.2%
n994
 
5.6%
l922
 
5.2%
s787
 
4.4%
Other values (53)5522
31.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)17799
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1805
 
10.1%
e1444
 
8.1%
t1412
 
7.9%
i1315
 
7.4%
a1265
 
7.1%
o1224
 
6.9%
r1109
 
6.2%
n994
 
5.6%
l922
 
5.2%
s787
 
4.4%
Other values (53)5522
31.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)17799
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1805
 
10.1%
e1444
 
8.1%
t1412
 
7.9%
i1315
 
7.4%
a1265
 
7.1%
o1224
 
6.9%
r1109
 
6.2%
n994
 
5.6%
l922
 
5.2%
s787
 
4.4%
Other values (53)5522
31.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)17799
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1805
 
10.1%
e1444
 
8.1%
t1412
 
7.9%
i1315
 
7.4%
a1265
 
7.1%
o1224
 
6.9%
r1109
 
6.2%
n994
 
5.6%
l922
 
5.2%
s787
 
4.4%
Other values (53)5522
31.0%

Duration Of Surgery
Real number (ℝ)

High correlation  Missing 

Distinct138
Distinct (%)26.7%
Missing6
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean69.97093
Minimum3
Maximum595
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2025-12-18T11:46:52.762172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile10
Q122
median45
Q390
95-th percentile215.25
Maximum595
Range592
Interquartile range (IQR)68

Descriptive statistics

Standard deviation71.869333
Coefficient of variation (CV)1.0271313
Kurtosis9.8546163
Mean69.97093
Median Absolute Deviation (MAD)27
Skewness2.5788191
Sum36105
Variance5165.2011
MonotonicityNot monotonic
2025-12-18T11:46:52.943582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2031
 
5.9%
2530
 
5.7%
1022
 
4.2%
1521
 
4.0%
4521
 
4.0%
4020
 
3.8%
3513
 
2.5%
5012
 
2.3%
3011
 
2.1%
6011
 
2.1%
Other values (128)324
62.1%
ValueCountFrequency (%)
31
 
0.2%
56
 
1.1%
72
 
0.4%
86
 
1.1%
92
 
0.4%
1022
4.2%
112
 
0.4%
126
 
1.1%
136
 
1.1%
142
 
0.4%
ValueCountFrequency (%)
5951
0.2%
4641
0.2%
4201
0.2%
3801
0.2%
3681
0.2%
3501
0.2%
3251
0.2%
3151
0.2%
2931
0.2%
2861
0.2%

Extubation Post OR
Real number (ℝ)

High correlation  Missing 

Distinct23
Distinct (%)8.2%
Missing240
Missing (%)46.0%
Infinite0
Infinite (%)0.0%
Mean10.113475
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2025-12-18T11:46:53.101583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q15
median10
Q312
95-th percentile20
Maximum50
Range49
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.2053727
Coefficient of variation (CV)0.61357472
Kurtosis6.6747742
Mean10.113475
Median Absolute Deviation (MAD)5
Skewness1.9059165
Sum2852
Variance38.506651
MonotonicityNot monotonic
2025-12-18T11:46:53.267805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1086
 
16.5%
585
 
16.3%
1530
 
5.7%
2018
 
3.4%
810
 
1.9%
258
 
1.5%
77
 
1.3%
35
 
1.0%
124
 
0.8%
24
 
0.8%
Other values (13)25
 
4.8%
(Missing)240
46.0%
ValueCountFrequency (%)
13
 
0.6%
24
 
0.8%
35
 
1.0%
41
 
0.2%
585
16.3%
63
 
0.6%
77
 
1.3%
810
 
1.9%
93
 
0.6%
1086
16.5%
ValueCountFrequency (%)
501
 
0.2%
351
 
0.2%
302
 
0.4%
258
 
1.5%
231
 
0.2%
2018
3.4%
191
 
0.2%
171
 
0.2%
161
 
0.2%
1530
5.7%

Blood Loss during surgery
Real number (ℝ)

High correlation  Missing 

Distinct16
Distinct (%)47.1%
Missing488
Missing (%)93.5%
Infinite0
Infinite (%)0.0%
Mean389.85294
Minimum25
Maximum2000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2025-12-18T11:46:53.409413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile30
Q1100
median250
Q3500
95-th percentile1370
Maximum2000
Range1975
Interquartile range (IQR)400

Descriptive statistics

Standard deviation438.55217
Coefficient of variation (CV)1.1249169
Kurtosis5.6309288
Mean389.85294
Median Absolute Deviation (MAD)175
Skewness2.2716001
Sum13255
Variance192328.01
MonotonicityNot monotonic
2025-12-18T11:46:53.546023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
5005
 
1.0%
1004
 
0.8%
2003
 
0.6%
503
 
0.6%
3003
 
0.6%
2503
 
0.6%
7002
 
0.4%
4002
 
0.4%
302
 
0.4%
1201
 
0.2%
Other values (6)6
 
1.1%
(Missing)488
93.5%
ValueCountFrequency (%)
251
 
0.2%
302
0.4%
503
0.6%
1004
0.8%
1201
 
0.2%
1501
 
0.2%
2003
0.6%
2503
0.6%
3003
0.6%
4002
0.4%
ValueCountFrequency (%)
20001
 
0.2%
15001
 
0.2%
13001
 
0.2%
7002
 
0.4%
6001
 
0.2%
5005
1.0%
4002
 
0.4%
3003
0.6%
2503
0.6%
2003
0.6%

Urine Output
Real number (ℝ)

High correlation  Missing 

Distinct15
Distinct (%)44.1%
Missing488
Missing (%)93.5%
Infinite0
Infinite (%)0.0%
Mean772.41176
Minimum32
Maximum3600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2025-12-18T11:46:53.689134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile282.5
Q1385
median600
Q3950
95-th percentile1740
Maximum3600
Range3568
Interquartile range (IQR)565

Descriptive statistics

Standard deviation655.11614
Coefficient of variation (CV)0.84814366
Kurtosis10.059782
Mean772.41176
Median Absolute Deviation (MAD)260
Skewness2.7485116
Sum26262
Variance429177.16
MonotonicityNot monotonic
2025-12-18T11:46:53.838029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
3006
 
1.1%
8005
 
1.0%
5005
 
1.0%
10003
 
0.6%
6003
 
0.6%
12002
 
0.4%
4002
 
0.4%
7001
 
0.2%
36001
 
0.2%
2501
 
0.2%
Other values (5)5
 
1.0%
(Missing)488
93.5%
ValueCountFrequency (%)
321
 
0.2%
2501
 
0.2%
3006
1.1%
3801
 
0.2%
4002
 
0.4%
5005
1.0%
6003
0.6%
7001
 
0.2%
8005
1.0%
10003
0.6%
ValueCountFrequency (%)
36001
 
0.2%
20001
 
0.2%
16001
 
0.2%
14001
 
0.2%
12002
 
0.4%
10003
0.6%
8005
1.0%
7001
 
0.2%
6003
0.6%
5005
1.0%

Pathology Findings
Categorical

High correlation 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
-
238 
no tumor
216 
tumor
68 

Length

Max length8
Median length5
Mean length4.4176245
Min length1

Characters and Unicode

Total characters2306
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno tumor
2nd rowno tumor
3rd row-
4th rowno tumor
5th row-

Common Values

ValueCountFrequency (%)
-238
45.6%
no tumor216
41.4%
tumor68
 
13.0%

Length

2025-12-18T11:46:54.012250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T11:46:54.085332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
tumor284
38.5%
238
32.2%
no216
29.3%

Most occurring characters

ValueCountFrequency (%)
o500
21.7%
m284
12.3%
u284
12.3%
t284
12.3%
r284
12.3%
-238
10.3%
216
9.4%
n216
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)2306
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o500
21.7%
m284
12.3%
u284
12.3%
t284
12.3%
r284
12.3%
-238
10.3%
216
9.4%
n216
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2306
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o500
21.7%
m284
12.3%
u284
12.3%
t284
12.3%
r284
12.3%
-238
10.3%
216
9.4%
n216
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2306
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o500
21.7%
m284
12.3%
u284
12.3%
t284
12.3%
r284
12.3%
-238
10.3%
216
9.4%
n216
9.4%

Tumor Category
Categorical

High correlation  Missing 

Distinct2
Distinct (%)2.9%
Missing454
Missing (%)87.0%
Memory size4.2 KiB
benign
38 
malignant
30 

Length

Max length9
Median length6
Mean length7.3235294
Min length6

Characters and Unicode

Total characters498
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbenign
2nd rowbenign
3rd rowbenign
4th rowbenign
5th rowbenign

Common Values

ValueCountFrequency (%)
benign38
 
7.3%
malignant30
 
5.7%
(Missing)454
87.0%

Length

2025-12-18T11:46:54.176036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T11:46:54.256331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
benign38
55.9%
malignant30
44.1%

Most occurring characters

ValueCountFrequency (%)
n136
27.3%
g68
13.7%
i68
13.7%
a60
12.0%
b38
 
7.6%
e38
 
7.6%
m30
 
6.0%
l30
 
6.0%
t30
 
6.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)498
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n136
27.3%
g68
13.7%
i68
13.7%
a60
12.0%
b38
 
7.6%
e38
 
7.6%
m30
 
6.0%
l30
 
6.0%
t30
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)498
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n136
27.3%
g68
13.7%
i68
13.7%
a60
12.0%
b38
 
7.6%
e38
 
7.6%
m30
 
6.0%
l30
 
6.0%
t30
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)498
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n136
27.3%
g68
13.7%
i68
13.7%
a60
12.0%
b38
 
7.6%
e38
 
7.6%
m30
 
6.0%
l30
 
6.0%
t30
 
6.0%

Pathology description
Text

Missing 

Distinct102
Distinct (%)35.8%
Missing237
Missing (%)45.4%
Memory size4.2 KiB
2025-12-18T11:46:54.496762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length56
Median length37
Mean length14.919298
Min length1

Characters and Unicode

Total characters4252
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique78 ?
Unique (%)27.4%

Sample

1st rowerosion
2nd rowinflammation
3rd rowlasceration-hematoma
4th rownecrosis
5th rowpilonidal sinus
ValueCountFrequency (%)
inflammation65
 
14.8%
hyperplasia32
 
7.3%
fibrosis27
 
6.2%
lipoma17
 
3.9%
carcinoma15
 
3.4%
cyst13
 
3.0%
cholelithiasis13
 
3.0%
adenocarcinoma11
 
2.5%
degenerative8
 
1.8%
fibrocartilage8
 
1.8%
Other values (141)230
52.4%
2025-12-18T11:46:54.909830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i506
11.9%
a501
11.8%
o327
 
7.7%
n296
 
7.0%
l285
 
6.7%
e270
 
6.3%
m255
 
6.0%
r237
 
5.6%
s234
 
5.5%
t221
 
5.2%
Other values (22)1120
26.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)4252
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i506
11.9%
a501
11.8%
o327
 
7.7%
n296
 
7.0%
l285
 
6.7%
e270
 
6.3%
m255
 
6.0%
r237
 
5.6%
s234
 
5.5%
t221
 
5.2%
Other values (22)1120
26.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4252
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i506
11.9%
a501
11.8%
o327
 
7.7%
n296
 
7.0%
l285
 
6.7%
e270
 
6.3%
m255
 
6.0%
r237
 
5.6%
s234
 
5.5%
t221
 
5.2%
Other values (22)1120
26.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4252
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i506
11.9%
a501
11.8%
o327
 
7.7%
n296
 
7.0%
l285
 
6.7%
e270
 
6.3%
m255
 
6.0%
r237
 
5.6%
s234
 
5.5%
t221
 
5.2%
Other values (22)1120
26.3%

Anesthesia type
Categorical

High correlation  Imbalance  Missing 

Distinct11
Distinct (%)2.1%
Missing8
Missing (%)1.5%
Memory size4.2 KiB
General
365 
Spinal
70 
Sedation and spinal
 
19
Sedation and local
 
17
Sedation
 
12
Other values (6)
 
31

Length

Max length20
Median length7
Mean length8.0700389
Min length5

Characters and Unicode

Total characters4148
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGeneral
2nd rowGeneral
3rd rowSedation
4th rowGeneral
5th rowLocal

Common Values

ValueCountFrequency (%)
General365
69.9%
Spinal70
 
13.4%
Sedation and spinal19
 
3.6%
Sedation and local17
 
3.3%
Sedation12
 
2.3%
RegionalRegional12
 
2.3%
Local5
 
1.0%
Regional5
 
1.0%
General and regional3
 
0.6%
Regional and local3
 
0.6%
(Missing)8
 
1.5%

Length

2025-12-18T11:46:55.031041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
general368
61.5%
spinal89
 
14.9%
sedation48
 
8.0%
and42
 
7.0%
local25
 
4.2%
regionalregional12
 
2.0%
regional11
 
1.8%
localregional3
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e822
19.8%
a613
14.8%
n585
14.1%
l543
13.1%
r371
8.9%
G368
8.9%
i175
 
4.2%
S118
 
2.8%
o114
 
2.7%
d90
 
2.2%
Other values (8)349
8.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)4148
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e822
19.8%
a613
14.8%
n585
14.1%
l543
13.1%
r371
8.9%
G368
8.9%
i175
 
4.2%
S118
 
2.8%
o114
 
2.7%
d90
 
2.2%
Other values (8)349
8.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4148
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e822
19.8%
a613
14.8%
n585
14.1%
l543
13.1%
r371
8.9%
G368
8.9%
i175
 
4.2%
S118
 
2.8%
o114
 
2.7%
d90
 
2.2%
Other values (8)349
8.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4148
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e822
19.8%
a613
14.8%
n585
14.1%
l543
13.1%
r371
8.9%
G368
8.9%
i175
 
4.2%
S118
 
2.8%
o114
 
2.7%
d90
 
2.2%
Other values (8)349
8.4%

Way Of Anesthesia
Categorical

High correlation  Imbalance  Missing 

Distinct3
Distinct (%)0.6%
Missing8
Missing (%)1.5%
Memory size4.2 KiB
Inhalation and IV
495 
IV
 
10
Inhalation
 
9

Length

Max length17
Median length17
Mean length16.585603
Min length2

Characters and Unicode

Total characters8525
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInhalation and IV
2nd rowInhalation and IV
3rd rowInhalation and IV
4th rowInhalation and IV
5th rowInhalation and IV

Common Values

ValueCountFrequency (%)
Inhalation and IV495
94.8%
IV10
 
1.9%
Inhalation9
 
1.7%
(Missing)8
 
1.5%

Length

2025-12-18T11:46:55.132741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T11:46:55.198436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
iv505
33.6%
inhalation504
33.5%
and495
32.9%

Most occurring characters

ValueCountFrequency (%)
n1503
17.6%
a1503
17.6%
I1009
11.8%
990
11.6%
V505
 
5.9%
h504
 
5.9%
l504
 
5.9%
i504
 
5.9%
t504
 
5.9%
o504
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)8525
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n1503
17.6%
a1503
17.6%
I1009
11.8%
990
11.6%
V505
 
5.9%
h504
 
5.9%
l504
 
5.9%
i504
 
5.9%
t504
 
5.9%
o504
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8525
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n1503
17.6%
a1503
17.6%
I1009
11.8%
990
11.6%
V505
 
5.9%
h504
 
5.9%
l504
 
5.9%
i504
 
5.9%
t504
 
5.9%
o504
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8525
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n1503
17.6%
a1503
17.6%
I1009
11.8%
990
11.6%
V505
 
5.9%
h504
 
5.9%
l504
 
5.9%
i504
 
5.9%
t504
 
5.9%
o504
 
5.9%

Complication During Surgery
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
no
519 
patient did not metabolize esmeron
 
1
bradycardia-rapid AFib
 
1
difficult intubation
 
1

Length

Max length34
Median length2
Mean length2.1340996
Min length2

Characters and Unicode

Total characters1114
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.6%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no519
99.4%
patient did not metabolize esmeron1
 
0.2%
bradycardia-rapid AFib1
 
0.2%
difficult intubation1
 
0.2%

Length

2025-12-18T11:46:55.302798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T11:46:55.373498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no519
98.3%
patient1
 
0.2%
did1
 
0.2%
not1
 
0.2%
metabolize1
 
0.2%
esmeron1
 
0.2%
bradycardia-rapid1
 
0.2%
afib1
 
0.2%
difficult1
 
0.2%
intubation1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n524
47.0%
o523
46.9%
i10
 
0.9%
a7
 
0.6%
t7
 
0.6%
6
 
0.5%
d6
 
0.5%
e5
 
0.4%
r4
 
0.4%
b4
 
0.4%
Other values (12)18
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)1114
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n524
47.0%
o523
46.9%
i10
 
0.9%
a7
 
0.6%
t7
 
0.6%
6
 
0.5%
d6
 
0.5%
e5
 
0.4%
r4
 
0.4%
b4
 
0.4%
Other values (12)18
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1114
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n524
47.0%
o523
46.9%
i10
 
0.9%
a7
 
0.6%
t7
 
0.6%
6
 
0.5%
d6
 
0.5%
e5
 
0.4%
r4
 
0.4%
b4
 
0.4%
Other values (12)18
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1114
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n524
47.0%
o523
46.9%
i10
 
0.9%
a7
 
0.6%
t7
 
0.6%
6
 
0.5%
d6
 
0.5%
e5
 
0.4%
r4
 
0.4%
b4
 
0.4%
Other values (12)18
 
1.6%

Blood Transfusion During Surgery
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
500 
True
 
22
ValueCountFrequency (%)
False500
95.8%
True22
 
4.2%
2025-12-18T11:46:55.436759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Number of transfused PC during surgery
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
0
500 
2
 
11
1
 
7
4
 
3
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters522
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row0
2nd row0
3rd row0
4th row3
5th row0

Common Values

ValueCountFrequency (%)
0500
95.8%
211
 
2.1%
17
 
1.3%
43
 
0.6%
31
 
0.2%

Length

2025-12-18T11:46:55.518143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T11:46:55.594909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0500
95.8%
211
 
2.1%
17
 
1.3%
43
 
0.6%
31
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0500
95.8%
211
 
2.1%
17
 
1.3%
43
 
0.6%
31
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)522
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0500
95.8%
211
 
2.1%
17
 
1.3%
43
 
0.6%
31
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)522
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0500
95.8%
211
 
2.1%
17
 
1.3%
43
 
0.6%
31
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)522
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0500
95.8%
211
 
2.1%
17
 
1.3%
43
 
0.6%
31
 
0.2%

Floor
Categorical

High correlation 

Distinct12
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
Third floor-New suite
149 
Third floor-extension
117 
Pediatric
81 
Fourth floor-New
74 
Second floor-OBS
50 
Other values (7)
51 

Length

Max length21
Median length21
Mean length17.091954
Min length3

Characters and Unicode

Total characters8922
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSecond floor-OBS
2nd rowICU
3rd rowICU
4th rowICU
5th rowICU

Common Values

ValueCountFrequency (%)
Third floor-New suite149
28.5%
Third floor-extension117
22.4%
Pediatric81
15.5%
Fourth floor-New74
14.2%
Second floor-OBS50
 
9.6%
Fourth floor-West16
 
3.1%
Fourth floor-East12
 
2.3%
ICU7
 
1.3%
TCU7
 
1.3%
Cardiac 24
 
0.8%
Other values (2)5
 
1.0%

Length

2025-12-18T11:46:55.691002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
third269
24.5%
floor-new223
20.3%
suite149
13.6%
floor-extension117
10.7%
fourth102
 
9.3%
pediatric81
 
7.4%
second50
 
4.6%
floor-obs50
 
4.6%
floor-west16
 
1.5%
floor-east12
 
1.1%
Other values (6)29
 
2.6%

Most occurring characters

ValueCountFrequency (%)
o1111
12.5%
r879
 
9.9%
e756
 
8.5%
i706
 
7.9%
576
 
6.5%
t480
 
5.4%
l421
 
4.7%
f421
 
4.7%
-421
 
4.7%
d406
 
4.6%
Other values (22)2745
30.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)8922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o1111
12.5%
r879
 
9.9%
e756
 
8.5%
i706
 
7.9%
576
 
6.5%
t480
 
5.4%
l421
 
4.7%
f421
 
4.7%
-421
 
4.7%
d406
 
4.6%
Other values (22)2745
30.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o1111
12.5%
r879
 
9.9%
e756
 
8.5%
i706
 
7.9%
576
 
6.5%
t480
 
5.4%
l421
 
4.7%
f421
 
4.7%
-421
 
4.7%
d406
 
4.6%
Other values (22)2745
30.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o1111
12.5%
r879
 
9.9%
e756
 
8.5%
i706
 
7.9%
576
 
6.5%
t480
 
5.4%
l421
 
4.7%
f421
 
4.7%
-421
 
4.7%
d406
 
4.6%
Other values (22)2745
30.8%

Antibiotics Post Operation
Boolean

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
True
347 
False
175 
ValueCountFrequency (%)
True347
66.5%
False175
33.5%
2025-12-18T11:46:55.757941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
410 
True
112 
ValueCountFrequency (%)
False410
78.5%
True112
 
21.5%
2025-12-18T11:46:55.805728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Analgesics Post Operation
Boolean

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
True
362 
False
160 
ValueCountFrequency (%)
True362
69.3%
False160
30.7%
2025-12-18T11:46:55.850867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Complication Post Surgery
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
485 
True
 
37
ValueCountFrequency (%)
False485
92.9%
True37
 
7.1%
2025-12-18T11:46:55.896658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Cardiac Complication
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
521 
True
 
1
ValueCountFrequency (%)
False521
99.8%
True1
 
0.2%
2025-12-18T11:46:55.937048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Type of cardiac Complication
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
no
521 
pericardial effusion
 
1

Length

Max length20
Median length2
Mean length2.0344828
Min length2

Characters and Unicode

Total characters1062
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no521
99.8%
pericardial effusion1
 
0.2%

Length

2025-12-18T11:46:56.018558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T11:46:56.085142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no521
99.6%
pericardial1
 
0.2%
effusion1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n522
49.2%
o522
49.2%
i3
 
0.3%
e2
 
0.2%
r2
 
0.2%
a2
 
0.2%
f2
 
0.2%
p1
 
0.1%
d1
 
0.1%
c1
 
0.1%
Other values (4)4
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)1062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n522
49.2%
o522
49.2%
i3
 
0.3%
e2
 
0.2%
r2
 
0.2%
a2
 
0.2%
f2
 
0.2%
p1
 
0.1%
d1
 
0.1%
c1
 
0.1%
Other values (4)4
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n522
49.2%
o522
49.2%
i3
 
0.3%
e2
 
0.2%
r2
 
0.2%
a2
 
0.2%
f2
 
0.2%
p1
 
0.1%
d1
 
0.1%
c1
 
0.1%
Other values (4)4
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n522
49.2%
o522
49.2%
i3
 
0.3%
e2
 
0.2%
r2
 
0.2%
a2
 
0.2%
f2
 
0.2%
p1
 
0.1%
d1
 
0.1%
c1
 
0.1%
Other values (4)4
 
0.4%

Pulmonary complication
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
515 
True
 
7
ValueCountFrequency (%)
False515
98.7%
True7
 
1.3%
2025-12-18T11:46:56.128798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Type of pulmonary complication
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
no
515 
atelectasis-pleural effsuion
 
3
pneumonia
 
3
RSV A/B
 
1

Length

Max length28
Median length2
Mean length2.1992337
Min length2

Characters and Unicode

Total characters1148
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no515
98.7%
atelectasis-pleural effsuion3
 
0.6%
pneumonia3
 
0.6%
RSV A/B1
 
0.2%

Length

2025-12-18T11:46:56.202689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T11:46:56.316174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no515
97.9%
atelectasis-pleural3
 
0.6%
effsuion3
 
0.6%
pneumonia3
 
0.6%
rsv1
 
0.2%
a/b1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n524
45.6%
o521
45.4%
e15
 
1.3%
a12
 
1.0%
l9
 
0.8%
s9
 
0.8%
u9
 
0.8%
i9
 
0.8%
t6
 
0.5%
p6
 
0.5%
Other values (12)28
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)1148
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n524
45.6%
o521
45.4%
e15
 
1.3%
a12
 
1.0%
l9
 
0.8%
s9
 
0.8%
u9
 
0.8%
i9
 
0.8%
t6
 
0.5%
p6
 
0.5%
Other values (12)28
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1148
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n524
45.6%
o521
45.4%
e15
 
1.3%
a12
 
1.0%
l9
 
0.8%
s9
 
0.8%
u9
 
0.8%
i9
 
0.8%
t6
 
0.5%
p6
 
0.5%
Other values (12)28
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1148
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n524
45.6%
o521
45.4%
e15
 
1.3%
a12
 
1.0%
l9
 
0.8%
s9
 
0.8%
u9
 
0.8%
i9
 
0.8%
t6
 
0.5%
p6
 
0.5%
Other values (12)28
 
2.4%

Renal complication
Boolean

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
522 
ValueCountFrequency (%)
False522
100.0%
2025-12-18T11:46:56.369461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Type of renal complication
Boolean

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
522 
ValueCountFrequency (%)
False522
100.0%
2025-12-18T11:46:56.401202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
522 
ValueCountFrequency (%)
False522
100.0%
2025-12-18T11:46:56.434793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Number of ransfused packet cells
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
0
517 
1
 
4
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters522
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0517
99.0%
14
 
0.8%
21
 
0.2%

Length

2025-12-18T11:46:56.508705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T11:46:56.577449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0517
99.0%
14
 
0.8%
21
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0517
99.0%
14
 
0.8%
21
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)522
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0517
99.0%
14
 
0.8%
21
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)522
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0517
99.0%
14
 
0.8%
21
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)522
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0517
99.0%
14
 
0.8%
21
 
0.2%

Neurological complication
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
520 
True
 
2
ValueCountFrequency (%)
False520
99.6%
True2
 
0.4%
2025-12-18T11:46:56.624094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Type of nurologic complication
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
no
520 
hemorrhagic CVA
 
1
seizure
 
1

Length

Max length15
Median length2
Mean length2.0344828
Min length2

Characters and Unicode

Total characters1062
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no520
99.6%
hemorrhagic CVA1
 
0.2%
seizure1
 
0.2%

Length

2025-12-18T11:46:56.702923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T11:46:56.769600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no520
99.4%
hemorrhagic1
 
0.2%
cva1
 
0.2%
seizure1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
o521
49.1%
n520
49.0%
e3
 
0.3%
r3
 
0.3%
h2
 
0.2%
i2
 
0.2%
m1
 
0.1%
a1
 
0.1%
g1
 
0.1%
c1
 
0.1%
Other values (7)7
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)1062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o521
49.1%
n520
49.0%
e3
 
0.3%
r3
 
0.3%
h2
 
0.2%
i2
 
0.2%
m1
 
0.1%
a1
 
0.1%
g1
 
0.1%
c1
 
0.1%
Other values (7)7
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o521
49.1%
n520
49.0%
e3
 
0.3%
r3
 
0.3%
h2
 
0.2%
i2
 
0.2%
m1
 
0.1%
a1
 
0.1%
g1
 
0.1%
c1
 
0.1%
Other values (7)7
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o521
49.1%
n520
49.0%
e3
 
0.3%
r3
 
0.3%
h2
 
0.2%
i2
 
0.2%
m1
 
0.1%
a1
 
0.1%
g1
 
0.1%
c1
 
0.1%
Other values (7)7
 
0.7%

Stroke
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
521 
True
 
1
ValueCountFrequency (%)
False521
99.8%
True1
 
0.2%
2025-12-18T11:46:56.822748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Type of stroke
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
no
521 
hemorrhagic
 
1

Length

Max length11
Median length2
Mean length2.0172414
Min length2

Characters and Unicode

Total characters1053
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no521
99.8%
hemorrhagic1
 
0.2%

Length

2025-12-18T11:46:56.896526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T11:46:56.955328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no521
99.8%
hemorrhagic1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
o522
49.6%
n521
49.5%
h2
 
0.2%
r2
 
0.2%
e1
 
0.1%
m1
 
0.1%
a1
 
0.1%
g1
 
0.1%
i1
 
0.1%
c1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1053
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o522
49.6%
n521
49.5%
h2
 
0.2%
r2
 
0.2%
e1
 
0.1%
m1
 
0.1%
a1
 
0.1%
g1
 
0.1%
i1
 
0.1%
c1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1053
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o522
49.6%
n521
49.5%
h2
 
0.2%
r2
 
0.2%
e1
 
0.1%
m1
 
0.1%
a1
 
0.1%
g1
 
0.1%
i1
 
0.1%
c1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1053
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o522
49.6%
n521
49.5%
h2
 
0.2%
r2
 
0.2%
e1
 
0.1%
m1
 
0.1%
a1
 
0.1%
g1
 
0.1%
i1
 
0.1%
c1
 
0.1%

Coma
Boolean

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
522 
ValueCountFrequency (%)
False522
100.0%
2025-12-18T11:46:56.999547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Major wound disruption
Boolean

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
522 
ValueCountFrequency (%)
False522
100.0%
2025-12-18T11:46:57.036542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Infection of the surgical site
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
509 
True
 
13
ValueCountFrequency (%)
False509
97.5%
True13
 
2.5%
2025-12-18T11:46:57.074390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Type of bacteria at the surgical site
Categorical

High correlation  Imbalance 

Distinct14
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
no
509 
klebsiella pneumonia CRE
 
1
klebsiella pneumonia-staphylococcus agalactiae
 
1
Ecoli ESBL
 
1
staphylococcus warneri-staphylococcus epidermidis-staphylococcus hominis
 
1
Other values (9)
 
9

Length

Max length86
Median length2
Mean length2.8448276
Min length2

Characters and Unicode

Total characters1485
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)2.5%

Sample

1st rowno
2nd rowno
3rd rowklebsiella pneumonia CRE
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no509
97.5%
klebsiella pneumonia CRE1
 
0.2%
klebsiella pneumonia-staphylococcus agalactiae1
 
0.2%
Ecoli ESBL1
 
0.2%
staphylococcus warneri-staphylococcus epidermidis-staphylococcus hominis1
 
0.2%
staphylococcus epidermidis-psuedomonas aeruginosa1
 
0.2%
enterococcus faecium1
 
0.2%
cryptococcus laurentii1
 
0.2%
serratia marcescens1
 
0.2%
staphylococcus coagulase negative1
 
0.2%
Other values (4)4
 
0.8%

Length

2025-12-18T11:46:57.159703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
no509
93.2%
staphylococcus4
 
0.7%
aeruginosa2
 
0.4%
klebsiella2
 
0.4%
enterococcus2
 
0.4%
pneumonia-staphylococcus1
 
0.2%
pneumonia1
 
0.2%
cre1
 
0.2%
esbl1
 
0.2%
ecoli1
 
0.2%
Other values (22)22
 
4.0%

Most occurring characters

ValueCountFrequency (%)
o545
36.7%
n529
35.6%
c48
 
3.2%
a46
 
3.1%
s42
 
2.8%
e36
 
2.4%
i34
 
2.3%
u26
 
1.8%
24
 
1.6%
l23
 
1.5%
Other values (20)132
 
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)1485
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o545
36.7%
n529
35.6%
c48
 
3.2%
a46
 
3.1%
s42
 
2.8%
e36
 
2.4%
i34
 
2.3%
u26
 
1.8%
24
 
1.6%
l23
 
1.5%
Other values (20)132
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1485
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o545
36.7%
n529
35.6%
c48
 
3.2%
a46
 
3.1%
s42
 
2.8%
e36
 
2.4%
i34
 
2.3%
u26
 
1.8%
24
 
1.6%
l23
 
1.5%
Other values (20)132
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1485
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o545
36.7%
n529
35.6%
c48
 
3.2%
a46
 
3.1%
s42
 
2.8%
e36
 
2.4%
i34
 
2.3%
u26
 
1.8%
24
 
1.6%
l23
 
1.5%
Other values (20)132
 
8.9%

Bacteremia
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
518 
True
 
4
ValueCountFrequency (%)
False518
99.2%
True4
 
0.8%
2025-12-18T11:46:57.224885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Type of bacteria in blood
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
no
518 
staphylococcus epidermidis-psuedomonas aeruginosa
 
1
achromobacter denitrificans-achromobacter xylosoxidans
 
1
Ecoli CRE
 
1
staphylococous hominis-staphylococcus epidermidis
 
1

Length

Max length54
Median length2
Mean length2.2931034
Min length2

Characters and Unicode

Total characters1197
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.8%

Sample

1st rowno
2nd rowno
3rd rowstaphylococcus epidermidis-psuedomonas aeruginosa
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no518
99.2%
staphylococcus epidermidis-psuedomonas aeruginosa1
 
0.2%
achromobacter denitrificans-achromobacter xylosoxidans1
 
0.2%
Ecoli CRE1
 
0.2%
staphylococous hominis-staphylococcus epidermidis1
 
0.2%

Length

2025-12-18T11:46:57.306008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T11:46:57.394328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no518
97.9%
staphylococcus1
 
0.2%
epidermidis-psuedomonas1
 
0.2%
aeruginosa1
 
0.2%
achromobacter1
 
0.2%
denitrificans-achromobacter1
 
0.2%
xylosoxidans1
 
0.2%
ecoli1
 
0.2%
cre1
 
0.2%
staphylococous1
 
0.2%
Other values (2)2
 
0.4%

Most occurring characters

ValueCountFrequency (%)
o536
44.8%
n524
43.8%
s15
 
1.3%
i14
 
1.2%
c14
 
1.2%
a12
 
1.0%
e9
 
0.8%
r8
 
0.7%
d7
 
0.6%
7
 
0.6%
Other values (15)51
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1197
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o536
44.8%
n524
43.8%
s15
 
1.3%
i14
 
1.2%
c14
 
1.2%
a12
 
1.0%
e9
 
0.8%
r8
 
0.7%
d7
 
0.6%
7
 
0.6%
Other values (15)51
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1197
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o536
44.8%
n524
43.8%
s15
 
1.3%
i14
 
1.2%
c14
 
1.2%
a12
 
1.0%
e9
 
0.8%
r8
 
0.7%
d7
 
0.6%
7
 
0.6%
Other values (15)51
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1197
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o536
44.8%
n524
43.8%
s15
 
1.3%
i14
 
1.2%
c14
 
1.2%
a12
 
1.0%
e9
 
0.8%
r8
 
0.7%
d7
 
0.6%
7
 
0.6%
Other values (15)51
 
4.3%

Other positive cultures related to surgery
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
518 
True
 
4
ValueCountFrequency (%)
False518
99.2%
True4
 
0.8%
2025-12-18T11:46:57.464820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Type of culture related to surgery
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
no
518 
urine
 
2
ascitis fluid
 
1
CSF
 
1

Length

Max length13
Median length2
Mean length2.0344828
Min length2

Characters and Unicode

Total characters1062
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no518
99.2%
urine2
 
0.4%
ascitis fluid1
 
0.2%
CSF1
 
0.2%

Length

2025-12-18T11:46:57.541200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T11:46:57.608258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no518
99.0%
urine2
 
0.4%
ascitis1
 
0.2%
fluid1
 
0.2%
csf1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n520
49.0%
o518
48.8%
i5
 
0.5%
u3
 
0.3%
r2
 
0.2%
e2
 
0.2%
s2
 
0.2%
a1
 
0.1%
c1
 
0.1%
t1
 
0.1%
Other values (7)7
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)1062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n520
49.0%
o518
48.8%
i5
 
0.5%
u3
 
0.3%
r2
 
0.2%
e2
 
0.2%
s2
 
0.2%
a1
 
0.1%
c1
 
0.1%
t1
 
0.1%
Other values (7)7
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n520
49.0%
o518
48.8%
i5
 
0.5%
u3
 
0.3%
r2
 
0.2%
e2
 
0.2%
s2
 
0.2%
a1
 
0.1%
c1
 
0.1%
t1
 
0.1%
Other values (7)7
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n520
49.0%
o518
48.8%
i5
 
0.5%
u3
 
0.3%
r2
 
0.2%
e2
 
0.2%
s2
 
0.2%
a1
 
0.1%
c1
 
0.1%
t1
 
0.1%
Other values (7)7
 
0.7%

BacteriaTypeRelatedToSurgery
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
no
518 
Ecoli CRE
 
1
Ecoli ESBL
 
1
candida parapsilosis
 
1
acinetobacter baumanii
 
1

Length

Max length22
Median length2
Mean length2.1015326
Min length2

Characters and Unicode

Total characters1097
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.8%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no518
99.2%
Ecoli CRE1
 
0.2%
Ecoli ESBL1
 
0.2%
candida parapsilosis1
 
0.2%
acinetobacter baumanii1
 
0.2%

Length

2025-12-18T11:46:57.703868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T11:46:57.786825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no518
98.5%
ecoli2
 
0.4%
cre1
 
0.2%
esbl1
 
0.2%
candida1
 
0.2%
parapsilosis1
 
0.2%
acinetobacter1
 
0.2%
baumanii1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
o522
47.6%
n521
47.5%
i8
 
0.7%
a8
 
0.7%
c5
 
0.5%
E4
 
0.4%
4
 
0.4%
l3
 
0.3%
s3
 
0.3%
d2
 
0.2%
Other values (12)17
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)1097
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o522
47.6%
n521
47.5%
i8
 
0.7%
a8
 
0.7%
c5
 
0.5%
E4
 
0.4%
4
 
0.4%
l3
 
0.3%
s3
 
0.3%
d2
 
0.2%
Other values (12)17
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1097
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o522
47.6%
n521
47.5%
i8
 
0.7%
a8
 
0.7%
c5
 
0.5%
E4
 
0.4%
4
 
0.4%
l3
 
0.3%
s3
 
0.3%
d2
 
0.2%
Other values (12)17
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1097
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o522
47.6%
n521
47.5%
i8
 
0.7%
a8
 
0.7%
c5
 
0.5%
E4
 
0.4%
4
 
0.4%
l3
 
0.3%
s3
 
0.3%
d2
 
0.2%
Other values (12)17
 
1.5%

Graft rejection
Boolean

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
522 
ValueCountFrequency (%)
False522
100.0%
2025-12-18T11:46:57.844867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Unplanned transfer to intensive care unit
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
520 
True
 
2
ValueCountFrequency (%)
False520
99.6%
True2
 
0.4%
2025-12-18T11:46:57.881052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Duration in intensive care unit (days)
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
0
520 
7
 
1
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters522
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0520
99.6%
71
 
0.2%
51
 
0.2%

Length

2025-12-18T11:46:57.963294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T11:46:58.035167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0520
99.6%
71
 
0.2%
51
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0520
99.6%
71
 
0.2%
51
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)522
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0520
99.6%
71
 
0.2%
51
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)522
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0520
99.6%
71
 
0.2%
51
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)522
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0520
99.6%
71
 
0.2%
51
 
0.2%

Sepsis
Boolean

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
522 
ValueCountFrequency (%)
False522
100.0%
2025-12-18T11:46:58.077003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Septic Shock
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
520 
True
 
2
ValueCountFrequency (%)
False520
99.6%
True2
 
0.4%
2025-12-18T11:46:58.115146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
0
522 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters522
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0522
100.0%

Length

2025-12-18T11:46:58.198902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T11:46:58.266709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0522
100.0%

Most occurring characters

ValueCountFrequency (%)
0522
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)522
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0522
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)522
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0522
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)522
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0522
100.0%

Collection
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
520 
True
 
2
ValueCountFrequency (%)
False520
99.6%
True2
 
0.4%
2025-12-18T11:46:58.306754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Unplanned Intubation
Boolean

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
522 
ValueCountFrequency (%)
False522
100.0%
2025-12-18T11:46:58.344949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

mv_48h_or_more
Boolean

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
522 
ValueCountFrequency (%)
False522
100.0%
2025-12-18T11:46:58.389965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

unplanned_return_to_or
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
512 
True
 
10
ValueCountFrequency (%)
False512
98.1%
True10
 
1.9%
2025-12-18T11:46:58.426804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

unplanned_or_reason
Categorical

High correlation  Imbalance 

Distinct7
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
no
512 
bleeding
 
4
CSF leak
 
2
collection
 
1
ICB
 
1
Other values (2)
 
2

Length

Max length21
Median length2
Mean length2.1417625
Min length2

Characters and Unicode

Total characters1118
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.8%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no512
98.1%
bleeding4
 
0.8%
CSF leak2
 
0.4%
collection1
 
0.2%
ICB1
 
0.2%
AV fistula thrombosis1
 
0.2%
EVD torn out1
 
0.2%

Length

2025-12-18T11:46:58.511211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T11:46:58.597873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no512
97.0%
bleeding4
 
0.8%
csf2
 
0.4%
leak2
 
0.4%
collection1
 
0.2%
icb1
 
0.2%
av1
 
0.2%
fistula1
 
0.2%
thrombosis1
 
0.2%
evd1
 
0.2%
Other values (2)2
 
0.4%

Most occurring characters

ValueCountFrequency (%)
n518
46.3%
o518
46.3%
e11
 
1.0%
l9
 
0.8%
i7
 
0.6%
6
 
0.5%
b5
 
0.4%
t5
 
0.4%
g4
 
0.4%
d4
 
0.4%
Other values (18)31
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1118
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n518
46.3%
o518
46.3%
e11
 
1.0%
l9
 
0.8%
i7
 
0.6%
6
 
0.5%
b5
 
0.4%
t5
 
0.4%
g4
 
0.4%
d4
 
0.4%
Other values (18)31
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1118
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n518
46.3%
o518
46.3%
e11
 
1.0%
l9
 
0.8%
i7
 
0.6%
6
 
0.5%
b5
 
0.4%
t5
 
0.4%
g4
 
0.4%
d4
 
0.4%
Other values (18)31
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1118
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n518
46.3%
o518
46.3%
e11
 
1.0%
l9
 
0.8%
i7
 
0.6%
6
 
0.5%
b5
 
0.4%
t5
 
0.4%
g4
 
0.4%
d4
 
0.4%
Other values (18)31
 
2.8%

other_complication
Categorical

High correlation  Imbalance 

Distinct6
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
no
512 
CSF leak
 
3
bleeding
 
3
meningitis
 
2
ICB
 
1

Length

Max length21
Median length2
Mean length2.137931
Min length2

Characters and Unicode

Total characters1116
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no512
98.1%
CSF leak3
 
0.6%
bleeding3
 
0.6%
meningitis2
 
0.4%
ICB1
 
0.2%
AV fistula thrombosis1
 
0.2%

Length

2025-12-18T11:46:58.718757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T11:46:58.803123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no512
97.2%
csf3
 
0.6%
leak3
 
0.6%
bleeding3
 
0.6%
meningitis2
 
0.4%
icb1
 
0.2%
av1
 
0.2%
fistula1
 
0.2%
thrombosis1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n519
46.5%
o514
46.1%
e11
 
1.0%
i11
 
1.0%
l7
 
0.6%
g5
 
0.4%
s5
 
0.4%
5
 
0.4%
C4
 
0.4%
t4
 
0.4%
Other values (15)31
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1116
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n519
46.5%
o514
46.1%
e11
 
1.0%
i11
 
1.0%
l7
 
0.6%
g5
 
0.4%
s5
 
0.4%
5
 
0.4%
C4
 
0.4%
t4
 
0.4%
Other values (15)31
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1116
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n519
46.5%
o514
46.1%
e11
 
1.0%
i11
 
1.0%
l7
 
0.6%
g5
 
0.4%
s5
 
0.4%
5
 
0.4%
C4
 
0.4%
t4
 
0.4%
Other values (15)31
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1116
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n519
46.5%
o514
46.1%
e11
 
1.0%
i11
 
1.0%
l7
 
0.6%
g5
 
0.4%
s5
 
0.4%
5
 
0.4%
C4
 
0.4%
t4
 
0.4%
Other values (15)31
 
2.8%

death_in_hospital_postop
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
False
518 
True
 
4
ValueCountFrequency (%)
False518
99.2%
True4
 
0.8%
2025-12-18T11:46:58.869954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

hospital_stay_days
Real number (ℝ)

High correlation 

Distinct30
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6666667
Minimum1
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2025-12-18T11:46:58.942859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33.75
95-th percentile11.95
Maximum65
Range64
Interquartile range (IQR)2.75

Descriptive statistics

Standard deviation5.6663468
Coefficient of variation (CV)1.5453673
Kurtosis39.140984
Mean3.6666667
Median Absolute Deviation (MAD)1
Skewness5.3473916
Sum1914
Variance32.107486
MonotonicityNot monotonic
2025-12-18T11:46:59.048614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1173
33.1%
2145
27.8%
373
14.0%
437
 
7.1%
525
 
4.8%
711
 
2.1%
610
 
1.9%
89
 
1.7%
97
 
1.3%
233
 
0.6%
Other values (20)29
 
5.6%
ValueCountFrequency (%)
1173
33.1%
2145
27.8%
373
14.0%
437
 
7.1%
525
 
4.8%
610
 
1.9%
711
 
2.1%
89
 
1.7%
97
 
1.3%
103
 
0.6%
ValueCountFrequency (%)
651
 
0.2%
451
 
0.2%
401
 
0.2%
351
 
0.2%
301
 
0.2%
291
 
0.2%
271
 
0.2%
261
 
0.2%
251
 
0.2%
233
0.6%

answered_call_followup
Boolean

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size654.0 B
True
300 
False
222 
ValueCountFrequency (%)
True300
57.5%
False222
42.5%
2025-12-18T11:46:59.122359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

complication_post_discharge
Boolean

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.6%
Missing212
Missing (%)40.6%
Memory size1.1 KiB
False
293 
True
 
17
(Missing)
212 
ValueCountFrequency (%)
False293
56.1%
True17
 
3.3%
(Missing)212
40.6%
2025-12-18T11:46:59.168612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

er_visit
Boolean

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.6%
Missing212
Missing (%)40.6%
Memory size1.1 KiB
False
301 
True
 
9
(Missing)
212 
ValueCountFrequency (%)
False301
57.7%
True9
 
1.7%
(Missing)212
40.6%
2025-12-18T11:46:59.212356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

post_discharge_complication_type
Categorical

High correlation  Imbalance  Missing 

Distinct14
Distinct (%)4.5%
Missing212
Missing (%)40.6%
Memory size4.2 KiB
no
293 
infection
 
4
meningitis
 
2
infection-collection
 
1
cholangitis
 
1
Other values (9)
 
9

Length

Max length31
Median length2
Mean length2.5451613
Min length2

Characters and Unicode

Total characters789
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)3.5%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no293
56.1%
infection4
 
0.8%
meningitis2
 
0.4%
infection-collection1
 
0.2%
cholangitis1
 
0.2%
inflamamtion1
 
0.2%
pneumonia1
 
0.2%
ICB1
 
0.2%
bleeding1
 
0.2%
infection-Av fistula thrombosis1
 
0.2%
Other values (4)4
 
0.8%
(Missing)212
40.6%

Length

2025-12-18T11:46:59.301056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
no293
93.6%
infection4
 
1.3%
meningitis2
 
0.6%
infection-collection1
 
0.3%
cholangitis1
 
0.3%
inflamamtion1
 
0.3%
pneumonia1
 
0.3%
icb1
 
0.3%
bleeding1
 
0.3%
infection-av1
 
0.3%
Other values (7)7
 
2.2%

Most occurring characters

ValueCountFrequency (%)
n321
40.7%
o310
39.3%
i34
 
4.3%
e17
 
2.2%
t17
 
2.2%
l11
 
1.4%
m11
 
1.4%
f10
 
1.3%
c9
 
1.1%
a9
 
1.1%
Other values (19)40
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)789
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n321
40.7%
o310
39.3%
i34
 
4.3%
e17
 
2.2%
t17
 
2.2%
l11
 
1.4%
m11
 
1.4%
f10
 
1.3%
c9
 
1.1%
a9
 
1.1%
Other values (19)40
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)789
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n321
40.7%
o310
39.3%
i34
 
4.3%
e17
 
2.2%
t17
 
2.2%
l11
 
1.4%
m11
 
1.4%
f10
 
1.3%
c9
 
1.1%
a9
 
1.1%
Other values (19)40
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)789
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n321
40.7%
o310
39.3%
i34
 
4.3%
e17
 
2.2%
t17
 
2.2%
l11
 
1.4%
m11
 
1.4%
f10
 
1.3%
c9
 
1.1%
a9
 
1.1%
Other values (19)40
 
5.1%

readmission_related_to_or
Boolean

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.6%
Missing212
Missing (%)40.6%
Memory size1.1 KiB
False
300 
True
 
10
(Missing)
212 
ValueCountFrequency (%)
False300
57.5%
True10
 
1.9%
(Missing)212
40.6%
2025-12-18T11:46:59.368756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

infection_or_inflammation
Boolean

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.6%
Missing212
Missing (%)40.6%
Memory size1.1 KiB
False
297 
True
 
13
(Missing)
212 
ValueCountFrequency (%)
False297
56.9%
True13
 
2.5%
(Missing)212
40.6%
2025-12-18T11:46:59.425879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

redo_surgery
Boolean

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.6%
Missing212
Missing (%)40.6%
Memory size1.1 KiB
False
309 
True
 
1
(Missing)
212 
ValueCountFrequency (%)
False309
59.2%
True1
 
0.2%
(Missing)212
40.6%
2025-12-18T11:46:59.469565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

admission_other_hospital
Boolean

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.6%
Missing212
Missing (%)40.6%
Memory size1.1 KiB
False
309 
True
 
1
(Missing)
212 
ValueCountFrequency (%)
False309
59.2%
True1
 
0.2%
(Missing)212
40.6%
2025-12-18T11:46:59.512004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

death_post_discharge
Boolean

Constant  Missing 

Distinct1
Distinct (%)0.3%
Missing212
Missing (%)40.6%
Memory size1.1 KiB
False
310 
(Missing)
212 
ValueCountFrequency (%)
False310
59.4%
(Missing)212
40.6%
2025-12-18T11:46:59.550906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

notes_description
Text

Missing 

Distinct52
Distinct (%)89.7%
Missing464
Missing (%)88.9%
Memory size4.2 KiB
2025-12-18T11:46:59.836594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length924
Median length169.5
Mean length207
Min length14

Characters and Unicode

Total characters12006
Distinct characters71
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)86.2%

Sample

1st rowThe patient admitted with ruptured abdominal aneurysm. Urgent repair of abdominal aortic dissection was done on 4.5.2025 by Dr.Mohamad Saab. CT on 15.4.2025: mild bilateral pleural effusion, lung basal atelectasis, large thrombosis AAA, hematoma, and severe pneumoperitoneum suggesting colonic perforation. Death was on 19.4.2025 due to septic shock and cardiac arrest.
2nd rowThe patient admitted with previous blast injury, infected bedsore, pneumonia, and seizure. Bacteremia, infected wound, and Septic shock were reported before surgery. He experienced tachycardia and tachypnea on 21.4.2025. Bacteremia on 4.4.2025: staphylococcus capitis. Bedsore culture (right and left foot) on 21.4.2025: heavy klebsiella pneumonia CRE. Blood culture on 21.4.2025: pseudomonas aeruginosa.
3rd rowThe patient admitted with multiomorbidities and multiorgan failure, infected kimal: staphylococcus capitis, sepsis, septic shock, CVA, pneumonia, leukocytosis, and UTI. Bacteremia on 25.2.2025 (hospital acquired): pseudomonas aeruginosa, on 19.4.2025: klebsiella pneumonia CRE, on 21.4.2025: staphylococcus epidermidis, on 23.5.2025: achromobacter denitrificans, and on 24.5.2025: achromobacter xylosoxidans. Death was on 27.5.2025 due to cardiogenic shock and sepsis.
4th rowThe patient readmitted under care of Dr. Hasan Shreim via ER on 21.6.2025: pus discharge at the site of surgery, subcutaneous collection and abscess, CRP=26.2, and umbilical culture on 21.6.2025: few klebsiella pneumonia and heavy staphylococcus agalactiae. Abscess drainage was urgently done on 21.6.2025.
5th rowChest CT on 13.4.2025 showed mild right pleural effusion, and bibasal atelectasis.
ValueCountFrequency (%)
on117
 
6.6%
and75
 
4.2%
the61
 
3.4%
was51
 
2.9%
of49
 
2.7%
patient48
 
2.7%
culture29
 
1.6%
dr27
 
1.5%
done27
 
1.5%
for22
 
1.2%
Other values (537)1278
71.6%
2025-12-18T11:47:00.309850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1729
 
14.4%
e1007
 
8.4%
a778
 
6.5%
o705
 
5.9%
n663
 
5.5%
t637
 
5.3%
r580
 
4.8%
i569
 
4.7%
s485
 
4.0%
d460
 
3.8%
Other values (61)4393
36.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)12006
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1729
 
14.4%
e1007
 
8.4%
a778
 
6.5%
o705
 
5.9%
n663
 
5.5%
t637
 
5.3%
r580
 
4.8%
i569
 
4.7%
s485
 
4.0%
d460
 
3.8%
Other values (61)4393
36.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12006
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1729
 
14.4%
e1007
 
8.4%
a778
 
6.5%
o705
 
5.9%
n663
 
5.5%
t637
 
5.3%
r580
 
4.8%
i569
 
4.7%
s485
 
4.0%
d460
 
3.8%
Other values (61)4393
36.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12006
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1729
 
14.4%
e1007
 
8.4%
a778
 
6.5%
o705
 
5.9%
n663
 
5.5%
t637
 
5.3%
r580
 
4.8%
i569
 
4.7%
s485
 
4.0%
d460
 
3.8%
Other values (61)4393
36.6%

Interactions

2025-12-18T11:46:29.788888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:14.734389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:17.438003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:19.970234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:22.883638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:26.308173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:28.961125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:31.883389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:34.457689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:38.914998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:41.287061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:43.788862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:46.838815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:49.627001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:52.518213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:55.552135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:58.060719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:00.683063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:04.035767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:07.090977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:09.457981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:11.950104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:14.685337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:17.579866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:20.203059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:23.632729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:26.350331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:29.893003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:14.869904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:17.532156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:20.303304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:22.970199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:26.420307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:29.061489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:31.974636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:34.552083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:39.011796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:41.371402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:43.883248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:46.925526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:49.751792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:52.620574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:55.643902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:58.148243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-18T11:46:17.209921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:19.811587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:23.263339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:25.946768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:29.401933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:32.060276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:17.152985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:19.687670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:22.590738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:26.028004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:28.664726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:31.581044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:34.177147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:38.646427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:40.986605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:43.510590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:46.550527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:49.244832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:52.251128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:55.262585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:57.776891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:00.387763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:03.721776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:06.809515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:09.165737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:11.659227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:14.275151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:17.302859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:19.908635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:23.352396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:26.043397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:29.495934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:32.147938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:17.254362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:19.788977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:22.689328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:26.119096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:28.765899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:31.698819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:34.274975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:38.737792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:41.101722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:43.604803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:46.649215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:49.376733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:52.335735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:55.365029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:57.877894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:00.485513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:03.849611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:06.899099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:09.279686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:11.760845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:14.432935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:17.392221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:20.001354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:23.446000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:26.154431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:29.596027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:32.244243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:17.343329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:19.871578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:22.785647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:26.218888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:28.856062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:31.783992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:34.361034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:38.823594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:41.196008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:43.700001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:46.740215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:49.495841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:52.425313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:55.458991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:45:57.971109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:00.580591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:03.935276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:07.000129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:09.374117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:11.854065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:14.562728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:17.489529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:20.098162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:23.541460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:26.255220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T11:46:29.695801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-18T11:47:00.608412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AFib-tachycardiaAnalgesics Post OperationAnesthesia typeAntibiotics Post OperationAnticoagulantAnticoagulants Post OperationAntidiabeticAntihypertensiveAntiplateletsAntipsychoticBUN before DischargeBUN day 1 post surgeryBacteremiaBacteriaTypeRelatedToSurgeryBetablockerBlood Loss during surgeryBlood Transfusion During SurgeryCAD HistoryCKDCOPDCardiac ComplicationCholesterol Lowering DrugCollectionComplication During SurgeryComplication Post SurgeryCreatinine before DischargeCreatinine_D1Current MedicationDiabetesMellitusDialysisDiuraticDuration Of SurgeryDuration in intensive care unit (days)DyslipidemiaER Admission Before SurgeryEmergency Status of surgeryExtubation Post ORFloorGastrointestinal DiseaseHB before dischargeHB day 1 post surgeryHFHypertensionInfection of the surgical siteNa before DischargeNa day 1 post surgeryNeurological complicationNeurological/ Psychological diseaseNumber of ransfused packet cellsNumber of transfused PC during surgeryOpen heart surgeryOther positive cultures related to surgeryOtherMedicationPADPathology FindingsPlatelet befor eDischargePlatelet day 1 post surgeryPre HBPre NaPre PlateletPre-BUNPre-CreatininePulmonary complicationRadiologySeptic ShockStrokeSurgeryThyroidal MedicationTumor CategoryType of Endocrine DiseaseType of Gastrointestinal DiseaseType of Neurological/ psychological diseaseType of bacteria at the surgical siteType of bacteria in bloodType of cardiac ComplicationType of culture related to surgeryType of medical ImagingType of nurologic complicationType of pulmonary complicationType of strokeUnplanned transfer to intensive care unitUrine OutputWay Of Anesthesiaadmission_other_hospitalageanswered_call_followupbmibmi_categorycomplication_post_dischargedeath_in_hospital_postoper_visitgendergovernorateheight_cmhospital_stay_daysid_numberinfection_or_inflammationinsurance_typeother_complicationpatient_namephysician_namepost_discharge_complication_typereadmission_related_to_orredo_surgerysmoking_statusunplanned_or_reasonunplanned_return_to_orweight_kg
AFib-tachycardia1.0000.0000.0000.0050.3130.0680.1280.1280.1330.0190.0000.2010.0000.1130.2470.6040.0000.1410.0570.0000.0000.0000.0000.1210.1500.0000.0000.1830.0850.0380.3040.0000.0000.0000.0400.0000.0000.2000.0000.0000.0000.8240.1500.0000.3750.0000.0000.1240.0230.0000.8150.0820.0000.8160.0000.0000.0000.0360.0000.0000.1450.1210.0070.0880.0000.0000.0000.0000.0000.0000.0000.1170.0000.0000.0000.0620.0460.0000.0160.0000.0000.4070.0000.0000.0750.0000.1950.0000.1960.0000.0280.1020.0000.0000.0000.0720.1340.0000.1040.0720.0000.2550.0000.0000.1430.0950.0000.000
Analgesics Post Operation0.0001.0000.2290.7820.0280.2990.0370.0840.0620.0740.6400.0000.0000.0000.0001.0000.1220.1040.0370.0170.0000.0000.0000.0000.1020.3830.0000.1170.0850.0000.0000.3650.0000.0000.1590.1130.2480.3750.0430.3560.1300.0270.0900.0500.7440.3840.0000.0600.0210.1090.0000.0000.0600.0680.1960.7420.0000.1730.1360.0950.0000.0320.0400.1620.0000.0000.4440.0000.0000.0000.0740.0200.0000.0000.0000.0330.1660.0370.0160.0000.0001.0000.0400.0000.3940.0320.2840.3060.0410.0000.0000.0000.1440.3820.1670.2700.0500.0000.0000.2700.2950.0000.0000.0000.1810.0230.0180.359
Anesthesia type0.0000.2291.0000.3100.1230.1720.1820.1620.2080.1150.4600.3980.1060.0000.2450.0000.0320.2450.3380.2570.0000.1640.0000.0000.0000.3930.4720.2010.1960.3150.1310.0000.0000.2110.0580.0000.0000.1430.1640.0000.0000.0000.2380.0000.0000.3230.0000.1110.0000.0000.0770.0000.1520.0000.2440.1320.2300.1780.2340.2380.2500.2910.0000.0380.0000.0000.2370.0000.1310.0000.1060.0000.0000.1250.0000.0000.1400.0000.0000.0000.0731.0000.3190.0000.1000.1080.1170.1250.0000.0000.0000.1030.0760.0000.1530.1300.0800.0620.1340.1300.1940.0000.1230.2810.0830.1360.1260.084
Antibiotics Post Operation0.0050.7820.3101.0000.0480.3040.0660.0650.0440.0340.6400.4250.0000.0000.0000.3370.1320.0770.0000.0000.0000.0240.0000.0000.1000.4970.5330.0620.0460.0000.0000.3550.0000.0000.1550.1010.2560.2860.0270.1730.1030.0360.0800.0600.4250.3220.0000.0000.0320.1210.0440.0000.0620.0940.2390.3940.0000.1440.0000.0380.1260.1250.0480.1670.0000.0000.4300.0000.0000.0000.0590.0740.0000.0000.0000.0000.2050.0000.0330.0000.0000.0000.0000.0000.3280.0000.2350.2350.0000.0000.0000.0000.1770.3350.0380.2990.0000.0000.0050.2990.3250.0720.0000.0000.1340.0000.0000.344
Anticoagulant0.3130.0280.1230.0481.0000.2220.2500.1470.1320.1060.0000.3590.0000.1670.0981.0000.1380.2580.1320.0000.0000.1370.0000.0000.1130.0000.0000.2580.2430.1080.0970.0000.1780.1060.0550.0000.2350.1860.0000.3110.0000.2270.2430.0140.3540.2620.0000.0900.0500.1730.1740.0000.0000.1960.0000.0000.1880.0570.1910.1160.4350.4030.1550.1340.0340.0000.1260.0000.0000.0000.0840.2260.2160.0000.0000.1010.1530.0000.2200.0000.0340.0000.0000.0000.3090.0550.2480.0000.0000.0000.0000.0000.0610.0000.1430.1050.0000.0000.1630.1050.0930.3290.0800.0000.0640.1570.0000.000
Anticoagulants Post Operation0.0680.2990.1720.3040.2221.0000.1980.1760.3060.1380.2920.0000.0760.0860.1760.2290.2230.3360.1820.1190.0000.1900.0680.0480.2610.2110.1240.2570.2430.0420.1100.3420.1010.0800.1300.0000.2350.3600.0000.1000.1090.2040.3050.1340.1940.1900.0000.0920.0990.2350.1190.0000.0000.1500.0990.3050.2960.2060.2320.0000.2480.1670.1140.1390.0000.0000.3740.0730.1660.0200.0450.2260.1860.1180.0000.0610.1620.0610.1520.0000.0680.2400.0570.0000.3990.0000.1280.1510.2280.0000.1560.0000.0960.1170.2600.2650.2150.0830.1650.2650.2410.2770.1840.0000.1170.1620.0670.151
Antidiabetic0.1280.0370.1820.0660.2500.1981.0000.3890.3760.0250.2860.2960.0000.1350.1930.0000.0000.3130.1410.0310.0190.3570.1040.0000.0370.2660.0000.4440.8710.0410.0650.0000.0950.2830.0000.0640.0000.1010.0000.1570.1010.1320.4110.0480.5950.4210.0000.0850.0750.0940.1880.0450.0000.2500.0000.4360.1790.2660.1250.1830.2380.2720.1190.0460.0000.0000.2020.1110.0000.1240.1350.2160.1680.0760.0190.1080.0600.0000.1530.0000.0000.0000.0000.0000.4180.0000.1570.1490.0000.0000.0000.0000.0000.0470.0000.1020.0000.0670.0000.1020.1310.0000.0000.0000.0570.0610.0000.241
Antihypertensive0.1280.0840.1620.0650.1470.1760.3891.0000.3410.0770.4220.0000.0000.1050.2510.0000.0490.3310.1810.1050.0000.2750.0830.0630.0000.0000.3390.5290.3340.1870.1420.1040.0740.2990.0000.0320.0400.1920.0330.3130.1580.1330.6960.0000.0000.1940.0000.0740.0000.0920.1150.0130.0000.1110.0000.5760.1520.0560.0460.1480.2270.2790.0000.0000.0000.0000.1710.0860.0000.1310.1370.1800.0000.0500.0000.0800.0660.0000.0000.0000.0000.0000.0280.0000.4420.0000.1460.1680.0000.0000.0000.0000.0610.1200.0490.0060.0000.0000.0000.0060.0000.1160.0000.0000.0760.0340.0000.173
Antiplatelets0.1330.0620.2080.0440.1320.3060.3760.3411.0000.0000.0880.3530.0000.0420.4440.2140.1200.5700.1850.2090.0000.4650.0000.1050.0710.1290.3040.5580.4280.1190.3310.1110.1110.3270.0000.1060.0670.2820.0180.2230.0000.2560.5080.0450.0000.1750.0000.0000.0370.1570.3020.0000.0280.2730.0690.0000.1680.2290.2300.0740.3360.2630.0000.0000.0000.0000.2930.1370.0570.0950.0710.2560.1390.0420.0000.0000.0000.0000.0670.0000.0760.0000.0000.0000.5500.0000.0990.1040.2020.0000.1120.0000.0770.1170.1190.1500.2650.0470.0510.1500.1640.2760.1920.0000.0940.0910.0000.171
Antipsychotic0.0190.0740.1150.0340.1060.1380.0250.0770.0001.0000.0000.0000.0000.1290.0000.0000.0000.0410.0000.0000.0000.0500.0000.0000.0000.0000.0000.3190.0230.0000.0000.0000.1420.0000.0300.0000.0000.0170.0000.0000.0000.0000.1120.0000.0000.2810.0000.5020.0000.0000.0000.0000.0430.0000.0670.4020.0000.1060.2580.2140.0000.0000.0000.0000.0000.0000.1460.0000.0000.1010.0000.6130.0250.1290.0000.0680.0590.0000.0210.0000.0000.0000.0000.0000.0630.0000.0000.0000.0000.0000.0000.0000.0000.0000.1230.0000.0000.0310.1250.0000.0000.1740.0000.0000.1060.1170.0000.000
BUN before Discharge0.0000.6400.4600.6400.0000.2920.2860.4220.0880.0001.0000.5920.3650.0000.382-0.3140.1310.2810.5160.7231.0000.0001.0001.0000.0000.8130.3730.4710.4150.5600.196-0.1770.0000.0000.3480.061-0.1330.3520.000-0.491-0.5820.2480.4480.0000.0420.1310.0000.2541.0000.0000.1820.0000.2540.0000.000-0.2730.082-0.3370.094-0.1190.6860.5900.0000.0001.0000.0000.3680.0000.0000.0000.0000.2540.0000.3971.0000.0000.0000.0000.0000.0000.000-0.0510.3941.0000.4390.0000.3300.0000.6310.2040.0000.0000.1180.1240.417-0.0050.0000.0000.000-0.0050.1360.0000.0001.0000.0000.0000.0000.300
BUN day 1 post surgery0.2010.0000.3980.4250.3590.0000.2960.0000.3530.0000.5921.0000.0000.0000.421-0.3430.1000.5200.6550.3630.0000.0000.0000.0000.3230.5070.6530.3070.4500.5630.679-0.1470.9390.0000.1810.0000.0690.0941.000-0.250-0.0970.5340.2270.2550.0790.1560.0000.0000.6140.0000.4020.0000.0000.7860.000-0.220-0.243-0.212-0.039-0.3450.6660.7040.4020.1710.3700.0000.3150.0000.0000.0001.0000.2350.2980.0000.0000.0000.3570.0000.4990.0000.939-0.3360.0000.0000.5070.0000.3980.0280.0000.0920.0720.1770.0000.159-0.0350.1660.2480.2260.2390.1660.0870.3780.4830.5000.0000.2080.1410.331
Bacteremia0.0000.0000.1060.0000.0000.0760.0000.0000.0000.0000.3650.0001.0000.4910.0000.0000.0000.0000.1060.0000.2440.0000.1670.0000.2720.1800.0000.0000.0300.0000.0000.0000.0000.0000.0000.0000.0000.3950.0000.0000.2840.0000.0500.0360.3110.0000.0000.0000.0000.0000.0000.1100.0000.0000.0000.0000.0000.0000.5020.1650.1470.3130.0730.0000.0000.0000.1520.0001.0000.0000.0000.0000.4730.9970.2440.4930.0000.0000.2740.0000.0001.0000.1440.0000.0910.0000.0000.0620.0000.1100.0580.0000.1520.0000.7280.1170.0230.0200.0000.1170.2720.5390.0490.0000.0000.0000.0000.000
BacteriaTypeRelatedToSurgery0.1130.0000.0000.0000.1670.0860.1350.1050.0420.1290.0000.0000.4911.0000.0500.0000.0000.0690.0000.1710.9970.0870.7020.0000.3060.0000.0000.0390.1180.0000.1960.0000.0000.0000.0400.1070.0000.2270.0000.0000.0000.1490.0600.2600.3020.1790.7020.1170.0000.0000.0000.9970.0000.0000.0000.2350.1680.1610.2940.0000.2000.0000.3660.0980.0000.9970.0000.2231.0000.0350.0000.0000.4760.4940.9970.9990.0670.7030.3220.9970.0001.0000.0000.0000.2030.0180.0000.0000.2230.4910.0000.0170.0680.0000.3850.0000.2600.0190.0000.0000.1130.2870.0000.0000.0000.4900.3010.000
Betablocker0.2470.0000.2450.0000.0980.1760.1930.2510.4440.0000.3820.4210.0000.0501.0000.0000.0490.4900.2450.1540.0000.3060.0000.0630.0390.2480.3110.5290.2210.1590.2990.0000.0740.2120.0000.0770.0570.1600.0330.2650.0000.2550.5500.0000.0000.0000.0000.0000.0000.1190.2070.0000.0000.0760.0210.2850.1240.2310.1170.0390.3030.3420.0000.0000.0000.0000.2540.1310.0000.1190.0820.1370.0000.0500.0000.0000.0000.0000.0000.0000.0000.0930.1340.0000.4860.0000.1930.1850.0790.0000.0000.1140.0770.1780.0740.1480.1280.0510.0550.1480.2060.1930.0660.0000.0420.0340.0000.179
Blood Loss during surgery0.6041.0000.0000.3371.0000.2290.0000.0000.2140.000-0.314-0.3430.0000.0000.0001.0000.5460.0000.0001.0000.0000.0000.0000.5990.141-0.577-0.5360.0000.0000.0000.0000.0361.0000.9190.2320.6220.0910.0001.000-0.179-0.1081.0000.0000.000-0.187-0.1481.0000.0000.0000.5011.0000.0000.0000.0000.000-0.185-0.205-0.216-0.0260.138-0.080-0.1400.0000.4181.0001.0000.0000.4020.4220.3771.0000.0000.0000.0000.0000.0000.3691.0000.0001.0001.0000.0801.0000.0000.0650.3190.2330.1270.0001.0001.0000.0000.367-0.3750.209-0.0610.0000.2250.000-0.0610.2620.0000.0001.0000.3100.0000.000-0.029
Blood Transfusion During Surgery0.0000.1220.0320.1320.1380.2230.0000.0490.1200.0000.1310.1000.0000.0000.0490.5461.0000.0000.0000.0000.0000.0370.0000.1950.0000.0000.0000.0880.0000.0000.0000.3380.2000.0640.0910.0000.1960.1230.0000.2350.0620.0690.0790.0000.0000.0000.0000.0000.0670.9970.0000.0000.0000.0000.0500.2060.0000.1760.0000.0650.0000.0000.0000.0960.0000.0000.1850.1140.0000.3070.0000.0000.1400.0000.0000.0000.1720.0000.0820.0000.0470.3830.0000.0000.1160.0000.0190.0000.0000.0000.0000.0650.0980.0000.2410.0870.0000.0460.0000.0870.0880.1840.0000.0000.0460.0000.0000.163
CAD History0.1410.1040.2450.0770.2580.3360.3130.3310.5700.0410.2810.5200.0000.0690.4900.0000.0001.0000.2680.1900.0000.4870.0000.1320.1010.3130.3000.4670.3730.1630.1750.0340.0890.3000.0000.0940.0000.2520.1020.2910.1630.1860.5100.0810.0790.1020.0000.0920.0670.0450.3480.0000.0000.1980.0000.3520.1530.1990.2040.1130.3860.3850.0000.0000.0000.0000.3310.0970.0520.0250.1760.3040.1870.0690.0000.0000.0000.0000.0910.0000.0000.0000.0690.0000.4730.0000.2060.1410.1520.0000.1050.0450.0950.0900.0420.1540.2030.0760.0690.1540.0690.2690.2020.0070.1000.1180.0000.173
CKD0.0570.0370.3380.0000.1320.1820.1410.1810.1850.0000.5160.6550.1060.0000.2450.0000.0000.2681.0000.2130.0000.0530.0210.0000.1240.7020.7500.2190.2730.7390.2900.0000.1600.0820.0000.0000.1800.2980.0000.4950.4160.2870.3060.0760.2490.1610.0000.0000.0330.0000.1570.0000.0000.1180.1380.2190.1040.4300.2470.1140.7380.8580.0600.0000.0000.0000.5370.0000.0000.0860.0000.0000.2540.2270.0000.0000.0000.0000.0970.0000.0210.0000.2180.0000.1800.0160.0840.0890.1410.0000.0570.0090.0220.0000.3750.1310.1800.0400.1450.1310.3910.3580.1390.0910.0000.2200.0270.118
COPD0.0000.0170.2570.0000.0000.1190.0310.1050.2090.0000.7230.3630.0000.1710.1541.0000.0000.1900.2131.0000.0000.0000.0000.0000.0000.3850.3490.1800.1260.2100.1490.0700.0000.0790.0000.0390.0000.2120.0000.5540.1880.2630.1730.0000.0000.0000.0000.0000.0000.0000.1860.0000.0000.0000.0000.0000.0000.2900.0970.0780.4250.3610.0000.0330.0000.0000.2580.0940.0000.2590.0000.0000.1130.1710.0000.1050.0000.0000.0000.0000.0000.0000.0960.0000.2450.0000.1670.0720.0000.0000.0000.0640.0000.1430.2600.0800.0200.0250.1670.0800.2260.1730.0000.1000.0790.1610.0000.000
Cardiac Complication0.0000.0000.0000.0000.0000.0000.0190.0000.0000.0001.0000.0000.2440.9970.0000.0000.0000.0000.0000.0001.0000.0000.3490.0000.0591.0000.0000.0000.0000.0000.0000.1880.0000.0000.0000.0000.0000.0000.0001.0000.0260.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.2440.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.1800.0000.0000.0000.0000.0651.0000.1660.0000.0000.0000.9970.4980.9980.0000.0000.5720.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0640.0000.3580.0090.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.000
Cholesterol Lowering Drug0.0000.0000.1640.0240.1370.1900.3570.2750.4650.0500.0000.0000.0000.0870.3060.0000.0370.4870.0530.0000.0001.0000.0000.0960.0870.0000.0000.4130.3770.0000.0440.0000.0000.4780.0000.0980.0000.2050.0250.1550.0000.0000.4240.1050.1470.4170.0000.0570.0000.1130.1230.0000.0000.0330.0000.0000.0000.0000.2350.0880.1410.0500.0000.0080.0000.0000.1780.1820.1860.1650.1090.2610.2240.0870.0000.0140.0690.0000.0000.0000.0000.0000.0000.0000.3640.0660.0870.1400.1550.0000.1440.0680.1200.1020.0000.1760.2020.0830.0000.1760.1300.2980.1910.0000.1190.0720.0000.111
Collection0.0000.0000.0000.0000.0000.0680.1040.0830.0000.0001.0000.0000.1670.7020.0000.0000.0000.0000.0210.0000.3490.0001.0000.0000.1581.0000.0000.0000.0920.0000.0000.2390.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0470.0781.0000.3770.0000.0000.0000.0000.0000.1670.0000.0000.0001.0000.0000.0000.0000.0000.0000.1160.1200.0000.0000.0000.0000.0191.0000.0900.0000.0000.6890.7020.3490.7030.0000.0000.3990.0000.0000.0000.0000.0000.0750.0000.0000.0000.0390.0000.0900.0000.0000.0000.2360.0000.0610.0000.0000.0000.0000.6770.0820.0000.0000.6990.0950.000
Complication During Surgery0.1210.0000.0000.0000.0000.0480.0000.0630.1050.0001.0000.0000.0000.0000.0630.5990.1950.1320.0000.0000.0000.0960.0001.0000.1401.0000.0000.0290.0000.0000.0000.4880.0000.1130.0000.0000.0040.1680.0001.0000.0000.0000.0250.0001.0000.0000.0000.1240.0000.1490.0000.0000.0000.0000.0561.0000.1510.0000.0000.1080.0000.0000.0000.0580.0000.0000.2120.1520.0000.0640.0000.1090.0000.0000.0000.0000.0230.0000.0000.0000.0000.6100.0000.0000.0590.0290.0000.0000.0000.0000.0000.0000.0850.0000.2160.0080.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.000
Complication Post Surgery0.1500.1020.0000.1000.1130.2610.0370.0000.0710.0000.0000.3230.2720.3060.0390.1410.0000.1010.1240.0000.0590.0870.1580.1401.0000.0990.0000.0340.0880.0000.1460.1600.2160.0230.0430.0000.0920.4590.0000.0000.0000.2070.1080.5530.2750.0000.1580.0000.1590.0000.0000.2720.0000.1630.0000.0000.0000.1090.3360.1000.2480.1800.3880.1160.1580.0590.1520.0870.0000.0000.0000.0000.5570.3060.0590.3090.1310.2160.4160.0590.1580.2000.0000.0730.2850.0920.0370.0000.7690.2720.4980.0490.0000.0000.3300.1040.6450.0500.4490.1040.2560.7990.6080.0730.0000.4950.4770.000
Creatinine before Discharge0.0000.3830.3930.4970.0000.2110.2660.0000.1290.0000.8130.5070.1800.0000.248-0.5770.0000.3130.7020.3851.0000.0001.0001.0000.0991.0000.4510.0000.3800.5080.297-0.2890.0000.0000.0000.0000.0040.2230.000-0.144-0.1770.2970.3770.1160.1140.0210.0000.0001.0000.0000.3970.0000.0000.1430.000-0.3060.163-0.1550.148-0.0160.5380.5850.0000.2130.0000.0000.1480.1350.0000.0000.0000.0000.0000.2301.0000.0000.0000.0000.0000.0000.0000.4000.5321.0000.3500.2050.5160.0220.1240.1530.0000.0000.0000.1390.263-0.0600.4410.0000.000-0.0600.1690.0000.0001.0000.0000.0000.0000.547
Creatinine_D10.0000.0000.4720.5330.0000.1240.0000.3390.3040.0000.3730.6530.0000.0000.311-0.5360.0000.3000.7500.3490.0000.0000.0000.0000.0000.4511.0000.2830.3290.7670.585-0.1380.4920.0000.2050.000-0.0420.0001.000-0.224-0.0350.5250.3480.0000.370-0.0650.0000.0000.2620.0000.4070.0000.3430.3410.000-0.097-0.280-0.089-0.136-0.3280.5850.8000.0000.0350.0000.0000.1890.0000.0000.3441.0000.0000.0000.0000.0000.0000.0310.0000.0400.0000.492-0.1000.0000.0000.2960.1660.2480.0000.0000.0000.0000.0000.000-0.085-0.133-0.0640.3000.0000.000-0.0640.0130.0000.0000.4570.0000.0000.0000.158
Current Medication0.1830.1170.2010.0620.2580.2570.4440.5290.5580.3190.4710.3070.0000.0390.5290.0000.0880.4670.2190.1800.0000.4130.0000.0290.0340.0000.2831.0000.4680.1460.2230.1130.0360.3550.0000.1450.1660.2640.0210.0000.0000.1690.6800.0210.1260.0830.0000.2000.0290.0890.1510.0000.1500.1610.1100.2490.1900.1750.1870.1430.3330.2320.0260.0270.0000.0000.2550.2900.1450.2660.0890.2290.0530.0390.0000.0580.0000.0000.0690.0000.0000.0000.0390.0000.6290.0000.2770.2820.0950.0000.0000.1070.0560.2760.1180.1280.1400.0520.0840.1280.1140.1400.0950.0000.1540.0790.0000.317
DiabetesMellitus0.0850.0850.1960.0460.2430.2430.8710.3340.4280.0230.4150.4500.0300.1180.2210.0000.0000.3730.2730.1260.0000.3770.0920.0000.0880.3800.3290.4681.0000.0570.1060.0000.0830.2980.0000.0820.0320.1920.0000.3460.1460.2080.4620.1100.4310.3490.0000.0550.0000.0760.2630.0300.0000.2600.0000.1910.0970.2940.3050.1740.3340.4030.1020.0000.0000.0000.3040.1090.0000.1350.0000.1870.2020.1180.0000.0930.0000.0000.1330.0000.0000.0000.0300.0000.4600.0000.1650.1480.0500.0000.0000.0000.0000.0840.0290.1110.0980.0830.0630.1110.1520.1750.0840.0000.0600.1100.0000.228
Dialysis0.0380.0000.3150.0000.1080.0420.0410.1870.1190.0000.5600.5630.0000.0000.1590.0000.0000.1630.7390.2100.0000.0000.0000.0000.0000.5080.7670.1460.0571.0000.2430.0000.0000.0000.0000.0000.3150.2900.0000.6340.3570.0860.2020.0000.2270.0000.0000.0000.0000.0000.0760.0000.0000.0000.0910.0000.1980.3030.0000.1670.6380.8700.0000.0000.0000.0000.5040.0000.0000.1640.0000.0000.0000.2080.0000.0000.0000.0000.0950.0000.0000.0000.1350.0000.0850.0240.0700.1170.0000.0000.0000.0210.0000.0000.3560.1850.0000.0800.0000.1850.2880.0000.0000.0000.0000.0000.0000.146
Diuratic0.3040.0000.1310.0000.0970.1100.0650.1420.3310.0000.1960.6790.0000.1960.2990.0000.0000.1750.2900.1490.0000.0440.0000.0000.1460.2970.5850.2230.1060.2431.0000.0000.2970.0000.0000.0000.3130.1740.0000.1120.2140.6540.2460.0000.3620.1090.0000.0000.0710.1210.3380.0000.0000.2970.0260.0000.1530.3640.0000.1870.4680.4780.1830.0000.0000.0000.1910.0170.0000.0000.0000.1240.1480.0000.0000.1240.0000.0000.2580.0000.2200.3000.0000.0000.3910.0190.0000.0000.1500.0000.0000.0000.0700.0000.1530.0530.1890.0640.1920.0530.2760.3720.1460.0960.0000.1870.0000.000
Duration Of Surgery0.0000.3650.0000.3550.0000.3420.0000.1040.1110.000-0.177-0.1470.0000.0000.0000.0360.3380.0340.0000.0700.1880.0000.2390.4880.160-0.289-0.1380.1130.0000.0000.0001.0000.0000.0270.0000.0000.3210.1860.0000.2100.2300.0000.0130.1130.0500.1640.1800.0500.0000.2890.0000.0000.0000.0000.172-0.039-0.2240.0520.039-0.137-0.036-0.0570.0000.1780.0000.0000.1720.0000.2210.0530.0000.0000.1870.0000.1880.0520.1830.1850.0000.0000.0000.4330.0000.1200.1860.0940.1630.0850.1980.0000.1490.0000.1700.1500.5300.0120.0900.0390.2540.0120.0240.1580.1330.0000.0820.1350.1970.178
Duration in intensive care unit (days)0.0000.0000.0000.0000.1780.1010.0950.0740.1110.1420.0000.9390.0000.0000.0741.0000.2000.0890.1600.0000.0000.0000.0000.0000.2160.0000.4920.0360.0830.0000.2970.0001.0000.1200.1100.0001.0000.1520.0000.0000.0650.2900.0770.0000.0000.0000.0000.0000.3480.2510.0000.0000.0000.2090.0000.0000.0000.2360.0000.0001.0000.4770.5290.1120.0000.0000.0000.1571.0000.1000.0000.0000.0000.0000.0000.0000.1710.0000.5730.0000.9991.0000.0000.0000.2990.0130.0000.0950.2230.0000.0000.0360.0000.0000.0760.0000.2600.0130.0000.0000.0670.6790.3020.0000.0000.0000.0000.130
Dyslipidemia0.0000.0000.2110.0000.1060.0800.2830.2990.3270.0000.0000.0000.0000.0000.2120.9190.0640.3000.0820.0790.0000.4780.0000.1130.0230.0000.0000.3550.2980.0000.0000.0270.1201.0000.0000.0830.0910.1400.0490.0000.2160.0000.3050.0000.0000.0000.0000.0000.0000.0820.0330.0000.0000.0000.0000.4040.1550.0000.0810.1830.0850.2250.0000.0000.0000.0000.0000.1640.0000.1470.1700.1500.1160.1050.0000.0000.0000.0000.0000.0000.0000.4070.0000.0000.3510.0000.1320.0930.1250.0000.0890.0300.1160.0610.0000.0980.1670.0400.0000.0980.0990.2940.1460.0000.0580.0920.0000.094
ER Admission Before Surgery0.0400.1590.0580.1550.0550.1300.0000.0000.0000.0300.3480.1810.0000.0400.0000.2320.0910.0000.0000.0000.0000.0000.0000.0000.0430.0000.2050.0000.0000.0000.0000.0000.1100.0001.0000.5110.0690.2610.0000.0520.0000.1470.0000.0000.0000.0000.0000.0000.1080.1200.0500.0000.0000.0000.1100.0000.0000.1850.2710.0000.0290.0000.1240.5680.0750.0000.2010.0000.0000.1480.0550.0750.1040.0400.0000.0590.6430.0670.1640.0000.0750.0000.0920.0000.0720.0000.0700.0290.0000.0850.0000.0000.0990.0000.2130.1420.0000.0000.0420.1420.3020.0920.0000.0000.0800.0880.0320.085
Emergency Status of surgery0.0000.1130.0000.1010.0000.0000.0640.0320.1060.0000.0610.0000.0000.1070.0770.6220.0000.0940.0000.0390.0000.0980.0000.0000.0000.0000.0000.1450.0820.0000.0000.0000.0000.0830.5111.0000.0000.1450.0000.1420.0000.0000.1300.0000.0000.0000.0000.0440.0000.1350.0000.0000.0000.0000.0760.1850.1920.0000.0880.0000.0000.0000.0000.3190.0000.0420.0780.0550.0000.0000.0000.0000.1190.0000.0000.1160.3690.1220.1180.0420.0000.0000.0880.0000.1650.0000.0000.0000.0000.0730.0000.0900.0550.0310.0710.1130.0000.0000.1180.1130.0830.0000.0000.0000.0000.2140.0610.070
Extubation Post OR0.0000.2480.0000.2560.2350.2350.0000.0400.0670.000-0.1330.0690.0000.0000.0570.0910.1960.0000.1800.0000.0000.0000.0000.0040.0920.004-0.0420.1660.0320.3150.3130.3211.0000.0910.0690.0001.0000.0000.1690.1160.1510.0000.0760.0380.3820.2370.1390.0000.0000.1210.1250.0000.0000.0000.127-0.181-0.0210.0170.153-0.0740.1210.0780.0000.0001.0001.0000.1950.0000.0000.0000.1790.0000.0660.0000.0000.0000.0000.1390.0001.0001.000-0.2031.0000.0000.2220.0540.0500.1130.1411.0000.0000.0000.0000.0470.347-0.0210.1890.1030.000-0.0210.0290.0000.0001.0000.0000.0000.0000.034
Floor0.2000.3750.1430.2860.1860.3600.1010.1920.2820.0170.3520.0940.3950.2270.1600.0000.1230.2520.2980.2120.0000.2050.0000.1680.4590.2230.0000.2640.1920.2900.1740.1860.1520.1400.2610.1450.0001.0000.0000.2760.1750.2240.2560.4230.2610.0670.5120.0000.0000.1650.0890.2140.0000.2800.1440.2200.0610.1840.3220.1480.2330.2740.2850.2200.2980.3470.2430.2110.0000.1290.0000.0170.3260.4020.0000.2260.1710.3480.1710.3470.1460.0000.1530.0000.3280.0540.2530.3850.4450.4700.3440.6250.1190.3030.3730.1300.4640.0780.1860.1300.2250.3550.2970.0000.2610.1950.2230.281
Gastrointestinal Disease0.0000.0430.1640.0270.0000.0000.0000.0330.0180.0000.0001.0000.0000.0000.0331.0000.0000.1020.0000.0000.0000.0250.0000.0000.0000.0001.0000.0210.0000.0000.0000.0000.0000.0490.0000.0000.1690.0001.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0560.0000.0520.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0820.0000.1190.1440.9960.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0620.0000.0000.0000.0000.0000.0000.0000.0000.0000.1270.0000.0000.0000.1270.0970.0000.0000.0000.0000.0000.0000.000
HB before discharge0.0000.3560.0000.1730.3110.1000.1570.3130.2230.000-0.491-0.2500.0000.0000.265-0.1790.2350.2910.4950.5541.0000.1551.0001.0000.000-0.144-0.2240.0000.3460.6340.1120.2100.0000.0000.0520.1420.1160.2760.0001.0000.8490.5370.2340.0810.0200.0220.3830.0000.0000.0000.3770.0000.0000.0000.000-0.027-0.1830.7440.191-0.157-0.473-0.3040.0000.0000.0000.0000.1710.0000.0000.0000.0000.0000.0970.0001.0000.0000.0000.4000.0000.0000.0000.3760.0001.000-0.0680.0000.2940.2000.1220.1320.3440.3390.1740.281-0.3220.2830.0000.0900.1000.283-0.1450.1100.0001.0000.1110.0000.0000.390
HB day 1 post surgery0.0000.1300.0000.1030.0000.1090.1010.1580.0000.000-0.582-0.0970.2840.0000.000-0.1080.0620.1630.4160.1880.0260.0000.0000.0000.000-0.177-0.0350.0000.1460.3570.2140.2300.0650.2160.0000.0000.1510.1750.0000.8491.0000.2080.0650.1370.0190.1590.0000.0000.1960.1160.0000.0000.0000.1820.000-0.061-0.0060.7880.254-0.098-0.214-0.1060.0000.1650.1000.0000.0000.0000.0000.0000.0000.0000.1900.1430.0260.0000.0330.0000.0570.0000.0000.1430.0000.000-0.0020.0000.2700.2440.0000.1900.0000.4680.0000.265-0.1310.0010.0000.2070.2330.001-0.2260.1070.2720.6010.0860.0000.2730.355
HF0.8240.0270.0000.0360.2270.2040.1320.1330.2560.0000.2480.5340.0000.1490.2551.0000.0690.1860.2870.2630.0000.0000.0000.0000.2070.2970.5250.1690.2080.0860.6540.0000.2900.0000.1470.0000.0000.2240.0000.5370.2081.0000.1860.0000.2730.0000.0000.1240.0640.0000.8280.1180.0000.8170.0000.0000.0000.2610.1760.0000.3930.2630.3220.1130.0000.0000.0000.1020.0000.0000.0000.1200.0700.0000.0000.0930.0690.0000.2070.0000.4160.0000.0000.0000.2130.0000.0000.0000.2370.0000.0000.0290.0000.0000.0940.0440.2830.0000.1430.0440.1580.3400.2030.3660.0130.1360.0140.000
Hypertension0.1500.0900.2380.0800.2430.3050.4110.6960.5080.1120.4480.2270.0500.0600.5500.0000.0790.5100.3060.1730.0000.4240.0470.0250.1080.3770.3480.6800.4620.2020.2460.0130.0770.3050.0000.1300.0760.2560.0000.2340.0650.1861.0000.0630.0000.2360.0000.0430.0000.0790.2260.0000.0000.1450.0000.1890.0000.1450.2230.0950.4060.3560.0820.0000.0470.0000.3360.0980.0000.1320.0870.2280.1170.0880.0000.0270.0000.0000.1180.0000.0470.0000.0760.0000.6520.0000.2720.2740.1330.0500.0580.0530.1010.2090.1270.1990.1600.0590.0790.1990.1850.1920.1780.0000.1270.0910.0000.245
Infection of the surgical site0.0000.0500.0000.0600.0140.1340.0480.0000.0450.0000.0000.2550.0360.2600.0000.0000.0000.0810.0760.0000.0000.1050.0780.0000.5530.1160.0000.0210.1100.0000.0000.1130.0000.0000.0000.0000.0380.4230.0000.0810.1370.0000.0631.0000.0000.2480.0780.0440.0000.0000.0000.0360.0000.2350.0000.0000.0000.1370.4620.0650.2090.2260.1350.0000.0780.1260.1710.0000.0000.0000.0000.1540.9880.2600.0000.2640.0000.2670.3040.1260.0000.0000.0320.0000.2280.0890.1830.0000.4850.1930.1580.0630.1380.0000.3700.2160.5620.0840.2570.2160.2420.8400.3990.1740.0000.4640.1970.000
Na before Discharge0.3750.7440.0000.4250.3540.1940.5950.0000.0000.0000.0420.0790.3110.3020.000-0.1870.0000.0790.2490.0001.0000.1471.0001.0000.2750.1140.3700.1260.4310.2270.3620.0500.0000.0000.0000.0000.3820.2610.0000.0200.0190.2730.0000.0001.0000.1770.2730.0001.0000.0000.0000.2730.1570.0000.235-0.059-0.2070.1740.294-0.2170.0910.2460.3540.0000.0000.4440.1820.0000.5810.0000.0000.0000.0000.0001.0000.3020.0000.3020.2820.4440.0000.3070.0001.0000.1140.0000.2830.2290.1960.0990.0000.1430.2310.040-0.030-0.1700.0000.1640.434-0.1700.1530.3610.4641.0000.0000.5000.4250.179
Na day 1 post surgery0.0000.3840.3230.3220.2620.1900.4210.1940.1750.2810.1310.1560.0000.1790.000-0.1480.0000.1020.1610.0000.0000.4170.3770.0000.0000.021-0.0650.0830.3490.0000.1090.1640.0000.0000.0000.0000.2370.0671.0000.0220.1590.0000.2360.2480.1771.0000.3920.0000.0000.2200.0000.2160.0000.0000.000-0.258-0.1430.1380.414-0.1170.0930.0160.0000.0000.2590.6260.0000.0760.0000.4151.0000.0390.3480.0000.0000.2820.0000.4050.0000.6260.000-0.1180.1170.000-0.0130.0000.1550.0450.1810.3120.3110.0000.1450.0340.2670.2690.0000.0000.3120.2690.1480.4400.5720.0000.0240.5120.4440.058
Neurological complication0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.7020.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.1580.0000.0000.0000.0000.0000.0000.1800.0000.0000.0000.0000.1390.5120.0000.3830.0000.0000.0000.0780.2730.3921.0000.0000.0000.0000.0000.1670.0000.0000.0000.2110.1040.0000.0000.0000.0000.0000.0000.0000.0000.3490.1270.0001.0000.0000.0000.0000.6890.0000.0000.7030.0500.9990.0000.3490.0000.0000.0000.0000.0000.0000.0000.0000.0960.1670.1490.0000.0990.0000.4850.0000.0000.1040.4890.0000.0000.6770.0000.0000.0000.6990.0950.000
Neurological/ Psychological disease0.1240.0600.1110.0000.0900.0920.0850.0740.0000.5020.2540.0000.0000.1170.0000.0000.0000.0920.0000.0000.0000.0570.0000.1240.0000.0000.0000.2000.0550.0000.0000.0500.0000.0000.0000.0440.0000.0000.0000.0000.0000.1240.0430.0440.0000.0000.0001.0000.0000.0000.1320.0000.0260.1300.0310.2680.0000.1140.1750.0000.0000.0000.0000.0000.0000.0000.0550.0000.0000.0000.0380.9900.1990.1170.0000.0570.0000.0000.0000.0000.0000.0000.0000.0000.0000.0390.0000.0470.0000.0000.0000.0470.0000.0000.1090.0000.0000.0000.0000.0000.0760.0000.0000.0000.0000.0000.0000.000
Number of ransfused packet cells0.0230.0210.0000.0320.0500.0990.0750.0000.0370.0001.0000.6140.0000.0000.0000.0000.0670.0670.0330.0000.0000.0000.0000.0000.1591.0000.2620.0290.0000.0000.0710.0000.3480.0000.1080.0000.0000.0000.0000.0000.1960.0640.0000.0001.0000.0000.0000.0001.0000.0470.0000.0000.0000.0720.0000.2340.0000.1550.0000.0000.0540.2420.1700.0380.0000.0000.0790.0000.0000.0000.0000.0000.0000.0000.0000.0000.0960.0000.1860.0000.3451.0000.0000.0000.0720.0670.3330.0000.0000.0000.0000.0950.0000.0000.0000.0430.0000.0000.3960.0430.0630.0000.0000.0000.0000.3370.3080.119
Number of transfused PC during surgery0.0000.1090.0000.1210.1730.2350.0940.0920.1570.0000.0000.0000.0000.0000.1190.5010.9970.0450.0000.0000.0000.1130.0000.1490.0000.0000.0000.0890.0760.0000.1210.2890.2510.0820.1200.1350.1210.1650.0000.0000.1160.0000.0790.0000.0000.2200.0000.0000.0471.0000.0000.0000.0000.0000.0500.3850.0000.0640.0000.0000.0000.0000.0990.1050.0000.0000.0790.2290.0000.3530.0000.0000.0000.0000.0000.0000.1030.0000.0860.0000.2480.3400.0000.0000.0930.0000.0000.0120.0380.0000.0000.0600.1070.0000.0990.0460.0740.0000.0000.0460.1140.2040.1050.0000.0810.0000.0000.066
Open heart surgery0.8150.0000.0770.0440.1740.1190.1880.1150.3020.0000.1820.4020.0000.0000.2071.0000.0000.3480.1570.1860.0000.1230.0000.0000.0000.3970.4070.1510.2630.0760.3380.0000.0000.0330.0500.0000.1250.0890.0000.3770.0000.8280.2260.0000.0000.0000.0000.1320.0000.0001.0000.0000.0000.8160.0380.0000.0000.1110.0530.0000.2600.2220.0000.0000.0000.0000.1120.0001.0000.0000.0000.3070.0490.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1440.0920.0000.0000.0910.0000.0000.0620.0000.0000.0000.0000.1200.0000.1340.0000.0000.3530.1490.5710.0000.1260.0000.000
Other positive cultures related to surgery0.0820.0000.0000.0000.0000.0000.0450.0130.0000.0000.0000.0000.1100.9970.0000.0000.0000.0000.0000.0000.2440.0000.1670.0000.2720.0000.0000.0000.0300.0000.0000.0000.0000.0000.0000.0000.0000.2140.0000.0000.0000.1180.0000.0360.2730.2160.1670.0000.0000.0000.0001.0000.0000.0000.0000.2050.0680.1480.2900.0000.1920.0000.0730.0000.0000.2440.0000.1041.0000.0000.0000.0000.4730.4910.2440.9980.1130.4950.2740.2440.0001.0000.0000.0000.2060.0000.0170.0960.0390.1100.0000.0000.0180.0000.3780.0000.0610.0200.0000.0000.0850.2810.0000.0000.0590.4870.0520.000
OtherMedication0.0000.0600.1520.0620.0000.0000.0000.0000.0280.0430.2540.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3430.1500.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0560.0000.0000.0000.0000.0000.1570.0000.0000.0260.0000.0000.0000.0001.0000.0000.0710.0000.0000.0000.0000.0000.0000.0000.0000.0270.0000.0000.0000.0000.1820.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0920.0000.0000.0800.0000.0000.0000.0790.0240.0000.0000.0000.0000.0570.0000.0000.0690.0000.0000.0000.3100.0000.0000.000
PAD0.8160.0680.0000.0940.1960.1500.2500.1110.2730.0000.0000.7860.0000.0000.0760.0000.0000.1980.1180.0000.0000.0330.0000.0000.1630.1430.3410.1610.2600.0000.2970.0000.2090.0000.0000.0000.0000.2800.0000.0000.1820.8170.1450.2350.0000.0000.0000.1300.0720.0000.8160.0000.0001.0000.0000.0880.1000.0860.1930.0000.1270.1330.0730.0000.0000.0000.2220.0261.0000.1600.0000.1110.2720.0000.0000.0000.0000.0000.0670.0000.2041.0000.0410.0000.0940.0000.0910.0000.1960.0000.0000.0000.0000.0000.0000.1180.2390.0000.0000.1180.0510.2820.0000.0000.0000.0000.0000.000
Pathology Findings0.0000.1960.2440.2390.0000.0990.0000.0000.0690.0670.0000.0000.0000.0000.0210.0000.0500.0000.1380.0000.0000.0000.0000.0560.0000.0000.0000.1100.0000.0910.0260.1720.0000.0000.1100.0760.1270.1440.0520.0000.0000.0000.0000.0000.2350.0000.0000.0310.0000.0500.0380.0000.0710.0001.0000.0000.1500.0770.1350.0340.0000.0000.0000.0000.0400.0000.5290.0001.0000.0680.0750.0460.0000.0000.0000.0000.1570.0000.0000.0000.0270.2630.0890.1170.1540.0360.0940.1010.0000.0000.0460.0490.0910.0430.0000.2990.0000.1390.0440.2990.3080.0000.0000.0000.0720.0000.0000.106
Platelet befor eDischarge0.0000.7420.1320.3940.0000.3050.4360.5760.0000.402-0.273-0.2200.0000.2350.285-0.1850.2060.3520.2190.0001.0000.0001.0001.0000.000-0.306-0.0970.2490.1910.0000.000-0.0390.0000.4040.0000.185-0.1810.2200.000-0.027-0.0610.0000.1890.000-0.059-0.2580.2110.2680.2340.3850.0000.2050.0000.0880.0001.0000.530-0.137-0.0290.387-0.0340.0060.0000.0000.0000.4380.1100.2160.0000.0000.0000.3390.0930.0001.0000.2350.1610.2400.0000.4380.000-0.2450.1071.0000.0380.000-0.0290.0000.0000.0660.0000.1870.369-0.111-0.135-0.0770.0000.3660.000-0.077-0.2440.0000.0001.0000.0000.2100.132-0.035
Platelet day 1 post surgery0.0000.0000.2300.0000.1880.2960.1790.1520.1680.0000.082-0.2430.0000.1680.124-0.2050.0000.1530.1040.0000.0000.0000.0000.1510.0000.163-0.2800.1900.0970.1980.153-0.2240.0000.1550.0000.192-0.0210.0610.000-0.183-0.0060.0000.0000.000-0.207-0.1430.1040.0000.0000.0000.0000.0680.0000.1000.1500.5301.000-0.155-0.1240.722-0.065-0.1650.0000.0000.0000.2470.0730.0000.4860.0000.0000.0000.0000.0000.0000.1380.0000.1360.0000.2470.000-0.0630.0000.000-0.1200.000-0.0220.0730.0000.1790.0000.1770.0000.089-0.0460.0610.0000.0000.2340.061-0.0020.0000.1500.0000.0000.2360.3150.068
Pre HB0.0360.1730.1780.1440.0570.2060.2660.0560.2290.106-0.337-0.2120.0000.1610.231-0.2160.1760.1990.4300.2900.0000.0000.0000.0000.109-0.155-0.0890.1750.2940.3030.3640.0520.2360.0000.1850.0000.0170.1840.0000.7440.7880.2610.1450.1370.1740.1380.0000.1140.1550.0640.1110.1480.0000.0860.077-0.137-0.1551.0000.340-0.284-0.1220.1930.2510.1860.0651.0000.1570.0450.0000.0000.0000.0000.1230.0000.0000.1320.0830.0000.2051.0000.2320.3410.0000.000-0.0300.0680.2320.1430.1170.0000.0000.4900.0890.497-0.170-0.0200.1750.0910.166-0.020-0.1560.1790.1200.5500.1560.1600.1160.406
Pre Na0.0000.1360.2340.0000.1910.2320.1250.0460.2300.2580.094-0.0390.5020.2940.117-0.0260.0000.2040.2470.0970.0000.2350.0000.0000.3360.148-0.1360.1870.3050.0000.0000.0390.0000.0810.2710.0880.1530.3220.0000.1910.2540.1760.2230.4620.2940.4140.0000.1750.0000.0000.0530.2900.0000.1930.135-0.029-0.1240.3401.000-0.1660.0630.0970.0000.2160.0001.0000.1490.1830.2080.0000.0000.0000.4810.5080.0000.2390.1660.0000.0001.0000.000-0.1400.0000.9800.1490.0000.1520.1740.4250.0000.0630.1790.1360.237-0.1130.0180.5170.0970.2290.018-0.0770.3150.4640.3910.0680.0670.0000.213
Pre Platelet0.0000.0950.2380.0380.1160.0000.1830.1480.0740.214-0.119-0.3450.1650.0000.0390.1380.0650.1130.1140.0780.0000.0880.0000.1080.100-0.016-0.3280.1430.1740.1670.187-0.1370.0000.1830.0000.000-0.0740.1480.000-0.157-0.0980.0000.0950.065-0.217-0.1170.0000.0000.0000.0000.0000.0000.0000.0000.0340.3870.722-0.284-0.1661.000-0.124-0.2210.0000.0000.0001.0000.0840.0000.2180.0000.0000.0000.1260.1760.0000.0000.0000.0000.0551.0000.0000.1990.0000.000-0.2240.093-0.1910.1720.0000.0000.1370.1340.040-0.318-0.0490.0560.0000.0720.1970.0560.0600.1720.1250.0000.1630.1710.085-0.262
Pre-BUN0.1450.0000.2500.1260.4350.2480.2380.2270.3360.0000.6860.6660.1470.2000.303-0.0800.0000.3860.7380.4250.0000.1410.0000.0000.2480.5380.5850.3330.3340.6380.468-0.0361.0000.0850.0290.0000.1210.2330.000-0.473-0.2140.3930.4060.2090.0910.0930.0000.0000.0540.0000.2600.1920.0000.1270.000-0.034-0.065-0.1220.063-0.1241.0000.5960.3640.0000.2951.0000.2830.0000.3600.1400.0000.0000.3040.1660.0000.2000.0000.0000.2571.0001.000-0.4970.1680.0000.5380.0960.1880.0920.2540.2710.0610.0590.0000.1050.176-0.0910.2980.0530.291-0.091-0.0320.3060.2810.6850.0000.3420.3490.159
Pre-Creatinine0.1210.0320.2910.1250.4030.1670.2720.2790.2630.0000.5900.7040.3130.0000.342-0.1400.0000.3850.8580.3610.0000.0500.1160.0000.1800.5850.8000.2320.4030.8700.478-0.0570.4770.2250.0000.0000.0780.2740.000-0.304-0.1060.2630.3560.2260.2460.0160.0000.0000.2420.0000.2220.0000.0000.1330.0000.006-0.1650.1930.097-0.2210.5961.0000.2390.0000.1161.0000.2820.0000.0820.0450.0000.0000.1840.3220.0000.0000.0000.0000.2301.0000.477-0.2200.2860.0000.3640.1560.2710.0870.2600.2860.2020.0560.0000.4140.073-0.1240.3170.0250.213-0.124-0.0890.2890.2590.5470.0000.2460.1740.374
Pulmonary complication0.0070.0400.0000.0480.1550.1140.1190.0000.0000.0000.0000.4020.0730.3660.0000.0000.0000.0000.0600.0000.1800.0000.1200.0000.3880.0000.0000.0260.1020.0000.1830.0000.5290.0000.1240.0000.0000.2850.0000.0000.0000.3220.0820.1350.3540.0000.0000.0000.1700.0990.0000.0730.0000.0730.0000.0000.0000.2510.0000.0000.3640.2391.0000.1280.1200.0000.0000.0580.0000.0870.0000.0000.5090.3660.1800.3680.2130.0000.9980.0000.3950.0000.0000.0000.3160.0000.0000.0810.0000.0730.0000.0000.0000.0000.2860.0000.0000.0000.0000.0000.2220.4550.0190.0000.0950.0000.0000.000
Radiology0.0880.1620.0380.1670.1340.1390.0460.0000.0000.0000.0000.1710.0000.0980.0000.4180.0960.0000.0000.0330.0000.0080.0000.0580.1160.2130.0350.0270.0000.0000.0000.1780.1120.0000.5680.3190.0000.2200.0000.0000.1650.1130.0000.0000.0000.0000.0000.0000.0380.1050.0000.0000.0270.0000.0000.0000.0000.1860.2160.0000.0000.0000.1281.0000.0770.0000.1900.0000.0000.0480.0680.0580.1090.0000.0000.0730.9970.0690.1680.0000.0770.0000.0000.0000.1350.0000.0000.0330.0000.0870.0000.0890.0000.0000.0960.0000.0000.0000.0600.0000.1790.0990.0000.0000.0120.1000.0000.000
Septic Shock0.0000.0000.0000.0000.0340.0000.0000.0000.0000.0001.0000.3700.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.1580.0000.0000.0000.0000.0000.0000.0000.0000.0000.0750.0001.0000.2980.0000.0000.1000.0000.0470.0780.0000.2590.0000.0000.0000.0000.0000.0000.0000.0000.0400.0000.0000.0650.0000.0000.2950.1160.1200.0771.0000.0000.0000.0001.0000.0000.0000.1620.6890.0000.0000.0000.1440.0000.3990.0000.0000.0000.0001.0000.3180.0000.0000.0001.0000.5271.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.2661.0001.0001.0000.0000.0000.0000.000
Stroke0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.9970.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0590.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0421.0000.3470.0000.0000.0000.0000.0000.1260.4440.6260.3490.0000.0000.0000.0000.2440.0000.0000.0000.4380.2471.0001.0001.0001.0001.0000.0000.0000.0001.0000.0170.0001.0000.0000.0000.0000.9880.0000.0000.9980.1270.9990.0000.4980.0001.0000.0001.0000.0000.0001.0001.0001.0000.2441.0000.0000.1791.0000.6980.0001.0000.0590.0000.0000.0721.0001.0001.0000.0000.9950.1471.000
Surgery0.0000.4440.2370.4300.1260.3740.2020.1710.2930.1460.3680.3150.1520.0000.2540.0000.1850.3310.5370.2580.0000.1780.0000.2120.1520.1480.1890.2550.3040.5040.1910.1720.0000.0000.2010.0780.1950.2430.0820.1710.0000.0000.3360.1710.1820.0000.1270.0550.0790.0790.1120.0000.0000.2220.5290.1100.0730.1570.1490.0840.2830.2820.0000.1900.0000.0171.0000.0000.5590.1860.0000.0000.0380.0760.0000.0070.2350.0180.0000.0170.0000.0000.2470.0600.1640.1510.1050.2240.2070.0000.0000.2250.1540.1320.1400.3930.2360.0820.0860.3930.3820.1270.1900.2140.0890.0560.1390.135
Thyroidal Medication0.0000.0000.0000.0000.0000.0730.1110.0860.1370.0000.0000.0000.0000.2230.1310.4020.1140.0970.0000.0940.0650.1820.0190.1520.0870.1350.0000.2900.1090.0000.0170.0000.1570.1640.0000.0550.0000.2110.0000.0000.0000.1020.0980.0000.0000.0760.0000.0000.0000.2290.0000.1040.0000.0260.0000.2160.0000.0450.1830.0000.0000.0000.0580.0000.0000.0000.0001.0000.0000.7250.1420.1770.0710.1460.0650.1880.0000.0000.0930.0000.0190.5000.0000.0000.2420.0000.0000.1010.1340.0000.0000.1080.0000.0000.0900.1470.1720.0520.0000.1470.0000.3230.1320.0000.0000.0000.0000.184
Tumor Category0.0000.0000.1310.0000.0000.1660.0000.0000.0570.0000.0000.0001.0001.0000.0000.4220.0000.0520.0000.0001.0000.1861.0000.0000.0000.0000.0000.1450.0000.0000.0000.2211.0000.0000.0000.0000.0000.0000.1190.0000.0000.0000.0000.0000.5810.0001.0000.0000.0000.0001.0001.0000.1821.0001.0000.0000.4860.0000.2080.2180.3600.0820.0000.0001.0001.0000.5590.0001.0000.0680.1700.0000.0001.0001.0001.0000.2341.0000.0001.0001.0000.0000.0000.0000.3200.2480.0000.0000.0001.0001.0000.0000.2840.0000.1000.2390.0000.0700.0000.2390.3970.0000.0001.0000.0000.0000.0000.000
Type of Endocrine Disease0.0000.0000.0000.0000.0000.0200.1240.1310.0950.1010.0000.0000.0000.0350.1190.3770.3070.0250.0860.2590.1660.1650.0900.0640.0000.0000.3440.2660.1350.1640.0000.0530.1000.1470.1480.0000.0000.1290.1440.0000.0000.0000.1320.0000.0000.4150.0000.0000.0000.3530.0000.0000.0000.1600.0680.0000.0000.0000.0000.0000.1400.0450.0870.0480.0000.0000.1860.7250.0681.0000.2730.0000.0000.0350.1660.0650.0000.0000.0000.0000.0900.4140.0000.0000.0000.0610.0000.0580.0000.0000.0000.1360.0370.0000.0840.0290.0000.0000.0000.0290.0520.0000.0740.0000.0000.0000.0000.000
Type of Gastrointestinal Disease0.0000.0740.1060.0590.0840.0450.1350.1370.0710.0000.0001.0000.0000.0000.0821.0000.0000.1760.0000.0000.0000.1090.0000.0000.0000.0001.0000.0890.0000.0000.0000.0000.0000.1700.0550.0000.1790.0000.9960.0000.0000.0000.0870.0000.0001.0000.0000.0380.0000.0000.0000.0000.0060.0000.0750.0000.0000.0000.0000.0000.0000.0000.0000.0680.0000.0000.0000.1420.1700.2731.0000.0000.0000.0000.0000.0000.0670.0000.0000.0000.0001.0000.0000.0000.0000.0380.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Type of Neurological/ psychological disease0.1170.0200.0000.0740.2260.2260.2160.1800.2560.6130.2540.2350.0000.0000.1370.0000.0000.3040.0000.0000.0000.2610.0000.1090.0000.0000.0000.2290.1870.0000.1240.0000.0000.1500.0750.0000.0000.0170.0000.0000.0000.1200.2280.1540.0000.0390.0000.9900.0000.0000.3070.0000.0000.1110.0460.3390.0000.0000.0000.0000.0000.0000.0000.0580.1620.0000.0000.1770.0000.0000.0001.0000.1940.0000.0000.0000.0890.0000.0000.0000.0000.0000.0000.0000.0770.0670.0000.0000.0870.0330.1760.1200.0520.0000.0000.0080.1230.1500.0000.0080.0790.1750.1610.0000.0000.0000.0000.000
Type of bacteria at the surgical site0.0000.0000.0000.0000.2160.1860.1680.0000.1390.0250.0000.2980.4730.4760.0000.0000.1400.1870.2540.1130.0000.2240.6890.0000.5570.0000.0000.0530.2020.0000.1480.1870.0000.1160.1040.1190.0660.3260.0000.0970.1900.0700.1170.9880.0000.3480.6890.1990.0000.0000.0490.4730.0000.2720.0000.0930.0000.1230.4810.1260.3040.1840.5090.1090.6890.9880.0380.0710.0000.0000.0000.1941.0000.4760.0000.5570.1030.6910.6490.9880.0000.0000.1560.0000.2040.0740.2180.0000.5640.6890.4420.0000.1540.0000.5260.0110.6560.0000.4210.0110.1360.7100.6090.9900.0000.6930.5210.000
Type of bacteria in blood0.0000.0000.1250.0000.0000.1180.0760.0500.0420.1290.3970.0000.9970.4940.0500.0000.0000.0690.2270.1710.9970.0870.7020.0000.3060.2300.0000.0390.1180.2080.0000.0000.0000.1050.0400.0000.0000.4020.0000.0000.1430.0000.0880.2600.0000.0000.0000.1170.0000.0000.0000.4910.0000.0000.0000.0000.0000.0000.5080.1760.1660.3220.3660.0000.0000.0000.0760.1461.0000.0350.0000.0000.4761.0000.9970.5720.0000.0000.3220.0000.0001.0000.2170.0000.0000.0180.0000.0000.2160.4910.3150.0170.1050.0000.7320.0100.2540.0190.0000.0100.2090.5420.2960.0000.0000.0000.0000.000
Type of cardiac Complication0.0000.0000.0000.0000.0000.0000.0190.0000.0000.0001.0000.0000.2440.9970.0000.0000.0000.0000.0000.0000.4980.0000.3490.0000.0591.0000.0000.0000.0000.0000.0000.1880.0000.0000.0000.0000.0000.0000.0001.0000.0260.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.2440.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.1800.0000.0000.0000.0000.0651.0000.1660.0000.0000.0000.9971.0000.9980.0000.0000.5720.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0640.0000.3580.0090.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.000
Type of culture related to surgery0.0620.0330.0000.0000.1010.0610.1080.0800.0000.0680.0000.0000.4930.9990.0000.0000.0000.0000.0000.1050.9980.0140.7030.0000.3090.0000.0000.0580.0930.0000.1240.0520.0000.0000.0590.1160.0000.2260.0000.0000.0000.0930.0270.2640.3020.2820.7030.0570.0000.0000.0000.9980.0000.0000.0000.2350.1380.1320.2390.0000.2000.0000.3680.0730.0000.9980.0070.1881.0000.0650.0000.0000.5570.5720.9981.0000.0620.7040.3250.9980.0001.0000.0000.0000.1580.0000.0000.0390.2230.4930.0000.0000.0970.0000.4490.0000.2600.0000.0000.0000.0910.2870.0000.0000.0000.5690.3050.000
Type of medical Imaging0.0460.1660.1400.2050.1530.1620.0600.0660.0000.0590.0000.3570.0000.0670.0000.3690.1720.0000.0000.0000.0000.0690.0000.0230.1310.0000.0310.0000.0000.0000.0000.1830.1710.0000.6430.3690.0000.1710.0000.0000.0330.0690.0000.0000.0000.0000.0500.0000.0960.1030.0000.1130.0000.0000.1570.1610.0000.0830.1660.0000.0000.0000.2130.9970.1440.1270.2350.0000.2340.0000.0670.0890.1030.0000.0000.0621.0000.0670.1980.1270.2570.0000.0360.3310.0980.0320.0000.0000.1080.1440.0000.1070.0280.0700.1020.1120.1330.0000.0740.1120.1870.1770.0000.0000.0590.0610.0900.000
Type of nurologic complication0.0000.0370.0000.0000.0000.0610.0000.0000.0000.0000.0000.0000.0000.7030.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.2160.0000.0000.0000.0000.0000.0000.1850.0000.0000.0670.1220.1390.3480.0000.4000.0000.0000.0000.2670.3020.4050.9990.0000.0000.0000.0000.4950.0000.0000.0000.2400.1360.0000.0000.0000.0000.0000.0000.0690.0000.9990.0180.0001.0000.0000.0000.0000.6910.0000.0000.7040.0671.0000.0000.9990.0000.0000.0000.0000.0000.0130.0000.0000.0960.4950.1490.0120.1080.0000.4870.0000.0000.0590.4910.0000.0000.6770.0000.0000.0000.7000.3080.000
Type of pulmonary complication0.0160.0160.0000.0330.2200.1520.1530.0000.0670.0210.0000.4990.2740.3220.0000.0000.0820.0910.0970.0000.5720.0000.3990.0000.4160.0000.0400.0690.1330.0950.2580.0000.5730.0000.1640.1180.0000.1710.0000.0000.0570.2070.1180.3040.2820.0000.0000.0000.1860.0860.0000.2740.0000.0670.0000.0000.0000.2050.0000.0550.2570.2300.9980.1680.3990.0000.0000.0930.0000.0000.0000.0000.6490.3220.5720.3250.1980.0001.0000.0000.8130.0000.0000.0000.2890.0000.0000.0710.1370.2740.0000.0600.0790.0000.2370.0420.1670.0000.0000.0420.1930.4570.1980.0000.0250.0000.0000.071
Type of stroke0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.9970.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0590.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0421.0000.3470.0000.0000.0000.0000.0000.1260.4440.6260.3490.0000.0000.0000.0000.2440.0000.0000.0000.4380.2471.0001.0001.0001.0001.0000.0000.0000.0000.4980.0170.0001.0000.0000.0000.0000.9880.0000.0000.9980.1270.9990.0001.0000.0001.0000.0001.0000.0000.0001.0001.0001.0000.2441.0000.0000.1791.0000.6980.0001.0000.0590.0000.0000.0721.0001.0001.0000.0000.9950.1471.000
Unplanned transfer to intensive care unit0.0000.0000.0730.0000.0340.0680.0000.0000.0760.0000.0000.9390.0000.0000.0001.0000.0470.0000.0210.0000.0000.0000.0000.0000.1580.0000.4920.0000.0000.0000.2200.0000.9990.0000.0750.0001.0000.1460.0000.0000.0000.4160.0470.0000.0000.0000.0000.0000.3450.2480.0000.0000.0000.2040.0270.0000.0000.2320.0000.0001.0000.4770.3950.0770.0000.0000.0000.0191.0000.0900.0000.0000.0000.0000.0000.0000.2570.0000.8130.0001.0001.0000.0000.0000.2960.0000.0000.0950.0390.0000.0000.0000.0000.0000.0620.0000.0610.0000.0000.0000.0510.6770.0820.0000.0000.0000.0000.130
Urine Output0.4071.0001.0000.0000.0000.2400.0000.0000.0000.000-0.051-0.3361.0001.0000.0930.0800.3830.0000.0000.0001.0000.0000.0000.6100.2000.400-0.1000.0000.0000.0000.3000.4331.0000.4070.0000.000-0.2030.0001.0000.3760.1430.0000.0000.0000.307-0.1180.0000.0001.0000.3400.0001.0000.0001.0000.263-0.245-0.0630.341-0.1400.199-0.497-0.2200.0000.0000.0001.0000.0000.5000.0000.4141.0000.0000.0001.0001.0001.0000.0000.0000.0001.0001.0001.0001.0000.000-0.1840.0000.1760.1580.0000.0000.0800.0000.0000.024-0.0900.2600.0000.0000.0000.260-0.0690.0000.0001.0000.0000.0000.0000.244
Way Of Anesthesia0.0000.0400.3190.0000.0000.0570.0000.0280.0000.0000.3940.0000.1440.0000.1341.0000.0000.0690.2180.0960.0000.0000.0000.0000.0000.5320.0000.0390.0300.1350.0000.0000.0000.0000.0920.0881.0000.1530.0000.0000.0000.0000.0760.0320.0000.1170.0000.0000.0000.0000.0000.0000.0000.0410.0890.1070.0000.0000.0000.0000.1680.2860.0000.0000.0000.0000.2470.0000.0000.0000.0000.0000.1560.2170.0000.0000.0360.0000.0000.0000.0001.0001.0000.0000.0390.0250.0000.0000.0000.1440.0000.0180.0000.0860.2700.0000.0000.0200.0000.0000.1090.0000.0000.0000.0000.0000.0000.000
admission_other_hospital0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0731.0000.0000.0000.0000.0000.0000.1200.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1171.0000.0000.0000.9800.0000.0000.0000.0000.0001.0001.0000.0600.0000.0000.0000.0000.0000.0000.0000.0000.0000.3310.0000.0001.0000.0000.0000.0001.0000.0000.0000.0000.0000.0961.0000.0000.0000.1180.0000.0000.0000.1170.0000.0000.0000.0000.6770.0000.0000.0000.0000.0000.000
age0.0750.3940.1000.3280.3090.3990.4180.4420.5500.0630.4390.5070.0910.2030.4860.0650.1160.4730.1800.2450.0000.3640.0750.0590.2850.3500.2960.6290.4600.0850.3910.1860.2990.3510.0720.1650.2220.3280.000-0.068-0.0020.2130.6520.2280.114-0.0130.0000.0000.0720.0930.1440.2060.0920.0940.1540.038-0.120-0.0300.149-0.2240.5380.3640.3160.1350.3180.0000.1640.2420.3200.0000.0000.0770.2040.0000.0000.1580.0980.0000.2890.0000.296-0.1840.0390.0001.0000.0550.4470.4700.4030.2080.1010.1200.0000.1670.308-0.0410.4760.0860.071-0.041-0.0040.2540.3570.1640.2740.0600.0680.400
answered_call_followup0.0000.0320.1080.0000.0550.0000.0000.0000.0000.0000.0000.0000.0000.0180.0000.3190.0000.0000.0160.0000.0000.0660.0000.0290.0920.2050.1660.0000.0000.0240.0190.0940.0130.0000.0000.0000.0540.0540.0620.0000.0000.0000.0000.0890.0000.0000.0000.0390.0670.0000.0920.0000.0000.0000.0360.0000.0000.0680.0000.0930.0960.1560.0000.0000.0000.0000.1510.0000.2480.0610.0380.0670.0740.0180.0000.0000.0320.0130.0000.0000.0000.0000.0250.0000.0551.0000.0000.0000.7170.0670.6740.0420.2280.0640.1250.0000.7350.0190.0110.0000.0810.9260.7410.0000.0420.0260.0000.000
bmi0.1950.2840.1170.2350.2480.1280.1570.1460.0990.0000.3300.3980.0000.0000.1930.2330.0190.2060.0840.1670.0000.0870.0000.0000.0370.5160.2480.2770.1650.0700.0000.1630.0000.1320.0700.0000.0500.2530.0000.2940.2700.0000.2720.1830.2830.1550.0000.0000.3330.0000.0000.0170.0000.0910.094-0.029-0.0220.2320.152-0.1910.1880.2710.0000.0000.0001.0000.1050.0000.0000.0000.0000.0000.2180.0000.0000.0000.0000.0000.0001.0000.0000.1760.0000.0000.4470.0001.0000.8530.1000.0000.0000.0000.0000.3040.137-0.0030.1440.0000.000-0.003-0.0230.1050.0000.0000.2100.0000.0590.881
bmi_category0.0000.3060.1250.2350.0000.1510.1490.1680.1040.0000.0000.0280.0620.0000.1850.1270.0000.1410.0890.0720.0000.1400.0000.0000.0000.0220.0000.2820.1480.1170.0000.0850.0950.0930.0290.0000.1130.3850.0000.2000.2440.0000.2740.0000.2290.0450.0000.0470.0000.0120.0000.0960.0800.0000.1010.0000.0730.1430.1740.1720.0920.0870.0810.0330.0001.0000.2240.1010.0000.0580.0000.0000.0000.0000.0000.0390.0000.0000.0711.0000.0950.1580.0000.0000.4700.0000.8531.0000.0430.0650.0430.0000.0190.4490.0000.1210.0000.0000.0000.1210.1570.0000.0000.0000.2170.0000.0960.683
complication_post_discharge0.1960.0410.0000.0000.0000.2280.0000.0000.2020.0000.6310.0000.0000.2230.0790.0000.0000.1520.1410.0000.0000.1550.0390.0000.7690.1240.0000.0950.0500.0000.1500.1980.2230.1250.0000.0000.1410.4450.0000.1220.0000.2370.1330.4850.1960.1810.0960.0000.0000.0380.0910.0390.0000.1960.0000.0000.0000.1170.4250.0000.2540.2600.0000.0001.0001.0000.2070.1340.0000.0000.0000.0870.5640.2160.0000.2230.1080.0960.1371.0000.0390.0000.0000.0960.4030.7170.1000.0431.0001.0000.6740.0250.1130.0000.0930.1210.8330.0960.4580.1210.1530.9800.7170.0960.1500.4230.2920.000
death_in_hospital_postop0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2040.0920.1100.4910.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.2720.1530.0000.0000.0000.0000.0000.0000.0000.0000.0850.0731.0000.4700.0000.1320.1900.0000.0500.1930.0990.3120.1670.0000.0000.0000.0000.1100.0000.0000.0000.0660.1790.0000.0000.0000.2710.2860.0730.0870.5270.2440.0000.0001.0000.0000.0000.0330.6890.4910.0000.4930.1440.4950.2740.2440.0000.0000.1441.0000.2080.0670.0000.0651.0001.0001.0000.0000.0000.0000.6000.0631.0000.0000.0000.0630.2851.0001.0001.0000.0000.4870.0520.000
er_visit0.0280.0000.0000.0000.0000.1560.0000.0000.1120.0000.0000.0720.0580.0000.0001.0000.0000.1050.0570.0000.0000.1440.0900.0000.4980.0000.0000.0000.0000.0000.0000.1490.0000.0890.0000.0000.0000.3440.0000.3440.0000.0000.0580.1580.0000.3110.1490.0000.0000.0000.0000.0000.0000.0000.0460.0000.0000.0000.0630.1370.0610.2020.0000.0001.0001.0000.0000.0001.0000.0000.0000.1760.4420.3150.0000.0000.0000.1490.0001.0000.0000.0800.0000.0000.1010.6740.0000.0430.6741.0001.0000.0000.1980.0000.0840.0670.4890.1890.4490.0670.0000.9510.5650.0000.2160.4490.1580.000
gender0.1020.0000.1030.0000.0000.0000.0000.0000.0000.0000.0000.1770.0000.0170.1140.0000.0650.0450.0090.0640.0000.0680.0000.0000.0490.0000.0000.1070.0000.0210.0000.0000.0360.0300.0000.0900.0000.6250.0000.3390.4680.0290.0530.0630.1430.0000.0000.0470.0950.0600.0620.0000.0790.0000.0490.1870.1770.4900.1790.1340.0590.0560.0000.0890.0000.0000.2250.1080.0000.1360.0000.1200.0000.0170.0000.0000.1070.0120.0600.0000.0000.0000.0180.0000.1200.0420.0000.0000.0250.0000.0001.0000.0330.6070.0000.0980.0000.0540.0290.0980.1660.0000.0470.0000.0240.0000.0250.326
governorate0.0000.1440.0760.1770.0610.0960.0000.0610.0770.0000.1180.0000.1520.0680.0770.3670.0980.0950.0220.0000.0640.1200.0000.0850.0000.0000.0000.0560.0000.0000.0700.1700.0000.1160.0990.0550.0000.1190.0000.1740.0000.0000.1010.1380.2310.1450.0990.0000.0000.1070.0000.0180.0240.0000.0910.3690.0000.0890.1360.0400.0000.0000.0000.0000.0000.1790.1540.0000.2840.0370.0000.0520.1540.1050.0640.0970.0280.1080.0790.1790.0000.0000.0000.1180.0000.2280.0000.0190.1130.0000.1980.0331.0000.0700.0970.0510.1620.2310.0000.0510.0810.2840.2050.0000.0470.0000.0000.000
height_cm0.0000.3820.0000.3350.0000.1170.0470.1200.1170.0000.1240.1590.0000.0000.178-0.3750.0000.0900.0000.1430.0000.1020.0000.0000.0000.139-0.0850.2760.0840.0000.0000.1500.0000.0610.0000.0310.0470.3030.0000.2810.2650.0000.2090.0000.0400.0340.0000.0000.0000.0000.0000.0000.0000.0000.043-0.1110.0890.4970.237-0.3180.1050.4140.0000.0000.0001.0000.1320.0000.0000.0000.0000.0000.0000.0000.0000.0000.0700.0000.0001.0000.0000.0240.0860.0000.1670.0640.3040.4490.0000.0000.0000.6070.0701.0000.1180.0180.0000.0330.0000.018-0.0430.0000.0000.0000.2410.0000.0000.654
hospital_stay_days0.0000.1670.1530.0380.1430.2600.0000.0490.1190.1230.417-0.0350.7280.3850.0740.2090.2410.0420.3750.2600.3580.0000.2360.2160.3300.263-0.1330.1180.0290.3560.1530.5300.0760.0000.2130.0710.3470.3730.000-0.322-0.1310.0940.1270.370-0.0300.2670.4850.1090.0000.0990.0000.3780.0000.0000.000-0.135-0.046-0.170-0.113-0.0490.1760.0730.2860.0960.0000.6980.1400.0900.1000.0840.0000.0000.5260.7320.3580.4490.1020.4870.2370.6980.062-0.0900.2700.0000.3080.1250.1370.0000.0930.6000.0840.0000.0970.1181.000-0.0630.0000.0000.084-0.0630.1300.0820.1830.0000.0600.2990.2310.136
id_number0.0720.2700.1300.2990.1050.2650.1020.0060.1500.000-0.0050.1660.1170.0000.148-0.0610.0870.1540.1310.0800.0090.1760.0000.0080.104-0.060-0.0640.1280.1110.1850.0530.0120.0000.0980.1420.113-0.0210.1300.1270.2830.0010.0440.1990.216-0.1700.2690.0000.0000.0430.0460.0000.0000.0000.1180.299-0.0770.061-0.0200.0180.056-0.091-0.1240.0000.0000.0000.0000.3930.1470.2390.0290.0000.0080.0110.0100.0090.0000.1120.0000.0420.0000.0000.2600.0000.000-0.0410.000-0.0030.1210.1210.0630.0670.0980.0510.018-0.0631.0000.1800.0690.0771.0000.8090.0730.0000.0000.0840.0440.000-0.011
infection_or_inflammation0.1340.0500.0800.0000.0000.2150.0000.0000.2650.0000.0000.2480.0230.2600.1280.0000.0000.2030.1800.0200.0000.2020.0610.0000.6450.4410.3000.1400.0980.0000.1890.0900.2600.1670.0000.0000.1890.4640.0000.0000.0000.2830.1600.5620.0000.0000.0000.0000.0000.0740.1200.0610.0000.2390.0000.0000.0000.1750.5170.0000.2980.3170.0000.0001.0001.0000.2360.1720.0000.0000.0000.1230.6560.2540.0000.2600.1330.0000.1671.0000.0610.0000.0000.1170.4760.7350.1440.0000.8331.0000.4890.0000.1620.0000.0000.1801.0000.0700.2420.1800.2200.9600.6430.1170.0000.3650.1180.000
insurance_type0.0000.0000.0620.0000.0000.0830.0670.0000.0470.0310.0000.2260.0200.0190.0510.2250.0460.0760.0400.0250.0000.0830.0000.0000.0500.0000.0000.0520.0830.0800.0640.0390.0130.0400.0000.0000.1030.0780.0000.0900.2070.0000.0590.0840.1640.0000.1040.0000.0000.0000.0000.0200.0570.0000.1390.3660.0000.0910.0970.0720.0530.0250.0000.0000.0000.0590.0820.0520.0700.0000.0000.1500.0000.0190.0000.0000.0000.0590.0000.0590.0000.0000.0200.0000.0860.0190.0000.0000.0960.0000.1890.0540.2310.0330.0000.0690.0701.0000.0730.0690.0000.1160.1140.0000.0410.0460.0000.000
other_complication0.1040.0000.1340.0050.1630.1650.0000.0000.0510.1250.0000.2390.0000.0000.0550.0000.0000.0690.1450.1670.0000.0000.0000.0000.4490.0000.0000.0840.0630.0000.1920.2540.0000.0000.0420.1180.0000.1860.0000.1000.2330.1430.0790.2570.4340.3120.4890.0000.3960.0000.1340.0000.0000.0000.0440.0000.2340.1660.2290.1970.2910.2130.0000.0600.0000.0000.0860.0000.0000.0000.0000.0000.4210.0000.0000.0000.0740.4910.0000.0000.0000.0000.0000.0000.0710.0110.0000.0000.4580.0000.4490.0290.0000.0000.0840.0770.2420.0731.0000.0770.1700.8180.5270.9930.3100.8230.7870.000
patient_name0.0720.2700.1300.2990.1050.2650.1020.0060.1500.000-0.0050.1660.1170.0000.148-0.0610.0870.1540.1310.0800.0090.1760.0000.0080.104-0.060-0.0640.1280.1110.1850.0530.0120.0000.0980.1420.113-0.0210.1300.1270.2830.0010.0440.1990.216-0.1700.2690.0000.0000.0430.0460.0000.0000.0000.1180.299-0.0770.061-0.0200.0180.056-0.091-0.1240.0000.0000.0000.0000.3930.1470.2390.0290.0000.0080.0110.0100.0090.0000.1120.0000.0420.0000.0000.2600.0000.000-0.0410.000-0.0030.1210.1210.0630.0670.0980.0510.018-0.0631.0000.1800.0690.0771.0000.8090.0730.0000.0000.0840.0440.000-0.011
physician_name0.0000.2950.1940.3250.0930.2410.1310.0000.1640.0000.1360.0870.2720.1130.2060.2620.0880.0690.3910.2260.0000.1300.0000.0000.2560.1690.0130.1140.1520.2880.2760.0240.0670.0990.3020.0830.0290.2250.097-0.145-0.2260.1580.1850.2420.1530.1480.0000.0760.0630.1140.0000.0850.0690.0510.308-0.244-0.002-0.156-0.0770.060-0.032-0.0890.2220.1790.2660.0720.3820.0000.3970.0520.0000.0790.1360.2090.0000.0910.1870.0000.1930.0720.051-0.0690.1090.000-0.0040.081-0.0230.1570.1530.2850.0000.1660.081-0.0430.1300.8090.2200.0000.1700.8091.0000.1590.1460.3050.1370.1120.195-0.059
post_discharge_complication_type0.2550.0000.0000.0720.3290.2770.0000.1160.2760.1740.0000.3780.5390.2870.1930.0000.1840.2690.3580.1730.0000.2980.6770.0000.7990.0000.0000.1400.1750.0000.3720.1580.6790.2940.0920.0000.0000.3550.0000.1100.1070.3400.1920.8400.3610.4400.6770.0000.0000.2040.3530.2810.0000.2820.0000.0000.0000.1790.3150.1720.3060.2890.4550.0991.0001.0000.1270.3230.0000.0000.0000.1750.7100.5420.0000.2870.1770.6770.4571.0000.6770.0000.0000.6770.2540.9260.1050.0000.9801.0000.9510.0000.2840.0000.0820.0730.9600.1160.8180.0730.1591.0000.9400.9800.4760.8180.7250.000
readmission_related_to_or0.0000.0000.1230.0000.0800.1840.0000.0000.1920.0000.0000.4830.0490.0000.0660.0000.0000.2020.1390.0000.0000.1910.0820.0000.6080.0000.0000.0950.0840.0000.1460.1330.3020.1460.0000.0000.0000.2970.0000.0000.2720.2030.1780.3990.4640.5720.0000.0000.0000.1050.1490.0000.0000.0000.0000.0000.1500.1200.4640.1250.2810.2590.0190.0001.0001.0000.1900.1320.0000.0740.0000.1610.6090.2960.0000.0000.0000.0000.1981.0000.0820.0000.0000.0000.3570.7410.0000.0000.7171.0000.5650.0470.2050.0000.1830.0000.6430.1140.5270.0000.1460.9401.0000.1400.1990.5710.3990.000
redo_surgery0.0000.0000.2810.0000.0000.0000.0000.0000.0000.0001.0000.5000.0000.0000.0001.0000.0000.0070.0910.1000.0000.0000.0000.0000.0731.0000.4570.0000.0000.0000.0960.0000.0000.0000.0000.0001.0000.0000.0001.0000.6010.3660.0000.1741.0000.0000.0000.0000.0000.0000.5710.0000.0000.0000.0001.0000.0000.5500.3910.0000.6850.5470.0000.0001.0001.0000.2140.0001.0000.0000.0000.0000.9900.0000.0000.0000.0000.0000.0001.0000.0001.0000.0000.0000.1640.0000.0000.0000.0961.0000.0000.0000.0000.0000.0000.0000.1170.0000.9930.0000.3050.9800.1401.0000.0000.9930.1740.000
smoking_status0.1430.1810.0830.1340.0640.1170.0570.0760.0940.1060.0000.0000.0000.0000.0420.3100.0460.1000.0000.0790.0000.1190.0000.0000.0000.0000.0000.1540.0600.0000.0000.0820.0000.0580.0800.0000.0000.2610.0000.1110.0860.0130.1270.0000.0000.0240.0000.0000.0000.0810.0000.0590.3100.0000.0720.0000.0000.1560.0680.1630.0000.0000.0950.0120.0000.0000.0890.0000.0000.0000.0000.0000.0000.0000.0000.0000.0590.0000.0250.0000.0000.0000.0000.0000.2740.0420.2100.2170.1500.0000.2160.0240.0470.2410.0600.0840.0000.0410.3100.0840.1370.4760.1990.0001.0000.3120.1370.252
unplanned_or_reason0.0950.0230.1360.0000.1570.1620.0610.0340.0910.1170.0000.2080.0000.4900.0340.0000.0000.1180.2200.1610.0000.0720.6990.0000.4950.0000.0000.0790.1100.0000.1870.1350.0000.0920.0880.2140.0000.1950.0000.0000.0000.1360.0910.4640.5000.5120.6990.0000.3370.0000.1260.4870.0000.0000.0000.2100.2360.1600.0670.1710.3420.2460.0000.1000.0000.9950.0560.0000.0000.0000.0000.0000.6930.0000.0000.5690.0610.7000.0000.9950.0000.0000.0000.0000.0600.0260.0000.0000.4230.4870.4490.0000.0000.0000.2990.0440.3650.0460.8230.0440.1120.8180.5710.9930.3121.0000.9950.000
unplanned_return_to_or0.0000.0180.1260.0000.0000.0670.0000.0000.0000.0000.0000.1410.0000.3010.0000.0000.0000.0000.0270.0000.0000.0000.0950.0000.4770.0000.0000.0000.0000.0000.0000.1970.0000.0000.0320.0610.0000.2230.0000.0000.2730.0140.0000.1970.4250.4440.0950.0000.3080.0000.0000.0520.0000.0000.0000.1320.3150.1160.0000.0850.3490.1740.0000.0000.0000.1470.1390.0000.0000.0000.0000.0000.5210.0000.0000.3050.0900.3080.0000.1470.0000.0000.0000.0000.0680.0000.0590.0960.2920.0520.1580.0250.0000.0000.2310.0000.1180.0000.7870.0000.1950.7250.3990.1740.1370.9951.0000.000
weight_kg0.0000.3590.0840.3440.0000.1510.2410.1730.1710.0000.3000.3310.0000.0000.179-0.0290.1630.1730.1180.0000.0000.1110.0000.0000.0000.5470.1580.3170.2280.1460.0000.1780.1300.0940.0850.0700.0340.2810.0000.3900.3550.0000.2450.0000.1790.0580.0000.0000.1190.0660.0000.0000.0000.0000.106-0.0350.0680.4060.213-0.2620.1590.3740.0000.0000.0001.0000.1350.1840.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0711.0000.1300.2440.0000.0000.4000.0000.8810.6830.0000.0000.0000.3260.0000.6540.136-0.0110.0000.0000.000-0.011-0.0590.0000.0000.0000.2520.0000.0001.000

Missing values

2025-12-18T11:46:32.771108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-18T11:46:33.756794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-12-18T11:46:34.789168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

id_numberpatient_namephone_numberphysician_nameadmission_dateagegendergovernoratemarital_statusinsurance_typeweight_kgheight_cmbmibmi_categoryblood_groupsmoking_statusReason for last hospital admissionAllergyHypertensionDiabetesMellitusDyslipidemiaCAD HistoryHFOpen heart surgeryAFib-tachycardiaPADCOPDCKDDialysisNeurological/ Psychological diseaseType of Neurological/ psychological diseaseGastrointestinal DiseaseType of Gastrointestinal DiseaseEndocrine DiseaseType of Endocrine DiseaseCancerType of cancerOther_HistoryCurrent MedicationAntihypertensiveAntiplateletsAnticoagulantAntidiabeticThyroidal MedicationAntipsychoticBetablockerCholesterol Lowering DrugDiuraticOtherMedicationReason of AdmissionICD10ER Admission Before SurgeryDiagnosis in ERRadiologyType of medical ImagingImaging DiagnosisPre-BUNBUN day 1 post surgeryBUN before DischargePre-CreatinineCreatinine_D1Creatinine before DischargePre NaNa day 1 post surgeryNa before DischargePre HBHB day 1 post surgeryHB before dischargePre PlateletPlatelet day 1 post surgeryPlatelet befor eDischargeDate of SurgeryCode of surgeonSurgeryEmergency Status of surgerySub Type Of The SurgeryDescriptio Of SurgeryDuration Of SurgeryExtubation Post ORBlood Loss during surgeryUrine OutputPathology FindingsTumor CategoryPathology descriptionAnesthesia typeWay Of AnesthesiaComplication During SurgeryBlood Transfusion During SurgeryNumber of transfused PC during surgeryFloorAntibiotics Post OperationAnticoagulants Post OperationAnalgesics Post OperationComplication Post SurgeryCardiac ComplicationType of cardiac ComplicationPulmonary complicationType of pulmonary complicationRenal complicationType of renal complicationBleeding post operation at the site of surgery that requires transfer of >= 4 packet cells within 72 hrs after surgeryNumber of ransfused packet cellsNeurological complicationType of nurologic complicationStrokeType of strokeComaMajor wound disruptionInfection of the surgical siteType of bacteria at the surgical siteBacteremiaType of bacteria in bloodOther positive cultures related to surgeryType of culture related to surgeryBacteriaTypeRelatedToSurgeryGraft rejectionUnplanned transfer to intensive care unitDuration in intensive care unit (days)SepsisSeptic ShockSystemic Inflammatory Response Syndrome (SISS)CollectionUnplanned Intubationmv_48h_or_moreunplanned_return_to_orunplanned_or_reasonother_complicationdeath_in_hospital_postophospital_stay_daysanswered_call_followupcomplication_post_dischargeer_visitpost_discharge_complication_typereadmission_related_to_orinfection_or_inflammationredo_surgeryadmission_other_hospitaldeath_post_dischargenotes_description
071659658900000115.02025-04-276.0maleBeqaanogovernment27.0123.017.8underweightNaNno smoking0nononononononononononononononono00000nononononononononononoleft severe hydronephrosis-left pyeloplastyN30nononono09.0NaNNaN0.48NaNNaN140.0NaNNaN13.6NaNNaN392.0NaNNaN2025-04-2815Urologic surgerynopyeloplastyleft pyeloplasty185.05.0NaNNaNno tumorNaNerosionGeneralInhalation and IVnono0Second floor-OBSyesnoyesnononononononono0nonononononononononononononono0nono0nonononononono4noNaNNaNNaNNaNNaNNaNNaNNaNNaN
1742812288900000168.02025-04-0580.0maleMount Lebanonyesgovernment70.0170.024.2normal7smokingnephrectomynoyesnononononononononononononono0000aortic aneurysmyesyesnononononoyesnononointestinal perforation-aortic dissection-ruptured abdominal aneurysm-urgent repair of abdominal aortic dissection was done on 4.5.2025 by Dr.Mohammad SaabNaNyesaneurysm-aortic dissectionyesCTruptured aneurysm-hematoma34.031.0NaN0.930.78NaN138.0142.0NaN10.511.9NaN236.0314.0NaN2025-04-1521General surgeryyescolectomyperforation of right colon-right hemicolectomy-anastomosis-ileostomy145.0NaNNaN300.0no tumorNaNinflammationGeneralInhalation and IVnono0ICUyesyesyesyesnonononononono0nonononononononononononononono0noyes0nonononononoyes5noNaNNaNNaNNaNNaNNaNNaNNaNThe patient admitted with ruptured abdominal aneurysm. Urgent repair of abdominal aortic dissection was done on 4.5.2025 by Dr.Mohamad Saab. CT on 15.4.2025: mild bilateral pleural effusion, lung basal atelectasis, large thrombosis AAA, hematoma, and severe pneumoperitoneum suggesting colonic perforation. Death was on 19.4.2025 due to septic shock and cardiac arrest.
270698698900000154.02025-03-1255.0maleBeirutyesprivate90.0190.024.9normalNaNno smokingcraniotomynonononononononononononoyesepilepsynono00000yesnononononoyesnonononoprevious blast injury-infected bedsore-pneumonia-seizure-aspiration-septic shock on yes4.3.2025-wound culture on yes3.3.2025: heavy proteus mirabilis ESBL-tissue culture on yes2.4.2025:heavy klebsiella oxytoca CRE-heavy providence rettgri ESBL-blood culture on 4.4.2025:staphylococcus capitisJ69.9-L89.9yesepilepsynono0NaN15.038.00.731.141.55153.0135.0142.011.37.89.333.0270.0214.02025-04-1220General surgerynoexcision of cyst-massexcision of necrotic tissue of bilateral feet22.0NaNNaNNaN-NaNNaNSedationInhalation and IVnono0ICUyesnoyesyesnonononononono0nonononononoyesklebsiella pneumonia CREyesstaphylococcus epidermidis-psuedomonas aeruginosanonononono0nono0nonononononono65yesnonononononononoThe patient admitted with previous blast injury, infected bedsore, pneumonia, and seizure. Bacteremia, infected wound, and Septic shock were reported before surgery. He experienced tachycardia and tachypnea on 21.4.2025. Bacteremia on 4.4.2025: staphylococcus capitis. Bedsore culture (right and left foot) on 21.4.2025: heavy klebsiella pneumonia CRE. Blood culture on 21.4.2025: pseudomonas aeruginosa.
370688688900000154.02025-04-0148.0femaleMount LebanonyesgovernmentNaNNaNNaNNaNNaNno smoking0noyesyesnononononononononononononoyeshyperthyroidism000yesyesyesnoyesyesnoyesyesnonoblast injury-amputation of left lower leg-spleen rupture-right leg fracture-eye injury-wound culture on yes.4.2025: few staphylococcus epidermidis, few staphylococcus warneri and few staphylococcus aureus methicillin resistantT14.8-S36.0-Y89.1-Y83.5yesblast injurynono014.09.0NaN0.560.510.40143.0144.0NaN10.69.4NaN305.0196.0NaN2025-04-012+38+33+52Orthopedic surgerynoamputationbilateral leg debridement-insertion of steinmenn pin right leg-amputation left foot-splenectomy-bleeding control-exploration of both eyes191.010.0600.0400.0no tumorNaNlasceration-hematomaGeneralInhalation and IVnoyes3ICUyesnoyesnononononononono0nonononononononononononononono0nono0nonononononono9noNaNNaNNaNNaNNaNNaNNaNNaNNaN
4747112718900000141.02025-04-1237.0femalenon lebaneseyesprivateNaNNaNNaNNaNNaNno smoking0nononononononononononononononono00000nononononononononononoaltered LOC-obstructive hydrocephaly-craniopharyngiomaNaNnononono08.014.027.00.480.431.54147.0151.0128.012.29.510.2282.0217.046.02025-04-2737NeurosurgerynoُEVDEVD insertion in the right ventricle33.0NaNNaNNaN-NaNNaNLocalNaNnono0ICUnonononononononononono0nonononononononononononononono0nono0nonononononono26noNaNNaNNaNNaNNaNNaNNaNNaNNaN
5747312738900000141.02025-04-1237.0femalenon lebaneseyesprivateNaNNaNNaNNaNNaNno smoking0nononononononononononononononono00000nononononononononononoaltered LOC-obstructive hydrocephaly-craniopharyngiomaNaNnononono0NaN17.027.00.420.481.54136.0147.0128.012.612.210.2460.0282.046.02025-04-2541NeurosurgerynoEVDcreation of external CSF shuntNaNNaNNaNNaN-NaNNaNNaNNaNnono0ICUnonononononononononono0nonononononononononononononono0nono0nonononononono27noNaNNaNNaNNaNNaNNaNNaNNaNNaN
670708708900000154.02024-12-0359.0femaleMount Lebanonyesgovernment58.0156.023.8normalNaNno smokingAV fistulanoyesnononononononoyesyesyesnononono00000nononononononononononodecrease LOC-multicomorbidities and multiorgan failure-infected kimal-sepsis-septic shock-CVA-pneumonia--fatigure-dizziness-leukocytosis-UTI-bacteremia on 3.yes2.2024: staphylococcus capitisN18.6-A41.9-R65.2yesinfected kimalnono028.0NaN45.04.71NaN1.96139.0NaN143.011.97.57.7413.0248.0145.02025-04-1433General surgerynodebridementsacral bedsore debridement-excision of decubitus ulcer18.0NaNNaNNaNno tumorNaNnecrosisGeneralInhalation and IVnono0Third floor-Suiteyesyesnonononononononono0nonononononononononononononono0nono0nonononononono45noNaNNaNNaNNaNNaNNaNNaNNaNNaN
7739311938900000131.02025-04-1434.0femaleMount Lebanonyesgovernment63.0156.025.9overweightNaNno smokingcholecystectomynononononononononononononononono00000nononononononononononoleft carpal tunnelNaNnononono010.0NaNNaN0.58NaNNaN142.0NaNNaN12.0NaNNaN214.0NaNNaN2025-04-1531Neurosurgerynonerve decompressionleft carpal tunnel31.0NaNNaNNaN-NaNNaNRegionalRegionalInhalation and IVnono0Third floor-Suitenonononononononononono0nonononononononononononononono0nono0nonononononono1yesnonononononononoNaN
870728728900000154.02024-12-0359.0femaleMount Lebanonyesgovernment58.0156.023.8normalNaNno smokingAV fistulanoyesnononononononoyesyesyesnononono00000nononononononononononodecrease LOC-multicomorbidities and multiorgan failure-infected kimal-sepsis-septic shock-CVA-pneumonia--fatigure-dizziness-leukocytosis-UTI-bacteremia on 3.yes2.2024: staphylococcus capitisN18.6-A41.9-R65.2nononono028.026.045.04.711.511.96139.0137.0143.011.97.87.7413.0338.0145.02025-04-248Vascular surgerynokimal insertionchange of infected kimal-kimal insertion8.0NaNNaNNaN-NaNNaNGeneralInhalationnono0Third floor-Suiteyesyesnoyesnonononononono0nonononononononoyesachromobacter denitrificans-achromobacter xylosoxidansnonononono0nono0nonononononoyes35noNaNNaNNaNNaNNaNNaNNaNNaNThe patient admitted with multiomorbidities and multiorgan failure, infected kimal: staphylococcus capitis, sepsis, septic shock, CVA, pneumonia, leukocytosis, and UTI. Bacteremia on 25.2.2025 (hospital acquired): pseudomonas aeruginosa, on 19.4.2025: klebsiella pneumonia CRE, on 21.4.2025: staphylococcus epidermidis, on 23.5.2025: achromobacter denitrificans, and on 24.5.2025: achromobacter xylosoxidans. Death was on 27.5.2025 due to cardiogenic shock and sepsis.
9720610068900000120.02025-04-2520.0malenon lebanesenoprivate70.0160.027.3overweightNaNsmokingtonsillectomynononononononononononononononono00000nononononononononononopilonidal cystL05nononono015.0NaNNaN0.93NaNNaN141.0NaNNaN17.0NaNNaN105.0NaNNaN2025-04-2620General surgerynoexcision of cyst-masspilonidal cyst excision10.0NaNNaNNaNno tumorNaNpilonidal sinusSpinalInhalation and IVnono0Third floor-New suiteyesnoyesnononononononono0nonononononononononononononono0nono0nonononononono2noNaNNaNNaNNaNNaNNaNNaNNaNNaN
id_numberpatient_namephone_numberphysician_nameadmission_dateagegendergovernoratemarital_statusinsurance_typeweight_kgheight_cmbmibmi_categoryblood_groupsmoking_statusReason for last hospital admissionAllergyHypertensionDiabetesMellitusDyslipidemiaCAD HistoryHFOpen heart surgeryAFib-tachycardiaPADCOPDCKDDialysisNeurological/ Psychological diseaseType of Neurological/ psychological diseaseGastrointestinal DiseaseType of Gastrointestinal DiseaseEndocrine DiseaseType of Endocrine DiseaseCancerType of cancerOther_HistoryCurrent MedicationAntihypertensiveAntiplateletsAnticoagulantAntidiabeticThyroidal MedicationAntipsychoticBetablockerCholesterol Lowering DrugDiuraticOtherMedicationReason of AdmissionICD10ER Admission Before SurgeryDiagnosis in ERRadiologyType of medical ImagingImaging DiagnosisPre-BUNBUN day 1 post surgeryBUN before DischargePre-CreatinineCreatinine_D1Creatinine before DischargePre NaNa day 1 post surgeryNa before DischargePre HBHB day 1 post surgeryHB before dischargePre PlateletPlatelet day 1 post surgeryPlatelet befor eDischargeDate of SurgeryCode of surgeonSurgeryEmergency Status of surgerySub Type Of The SurgeryDescriptio Of SurgeryDuration Of SurgeryExtubation Post ORBlood Loss during surgeryUrine OutputPathology FindingsTumor CategoryPathology descriptionAnesthesia typeWay Of AnesthesiaComplication During SurgeryBlood Transfusion During SurgeryNumber of transfused PC during surgeryFloorAntibiotics Post OperationAnticoagulants Post OperationAnalgesics Post OperationComplication Post SurgeryCardiac ComplicationType of cardiac ComplicationPulmonary complicationType of pulmonary complicationRenal complicationType of renal complicationBleeding post operation at the site of surgery that requires transfer of >= 4 packet cells within 72 hrs after surgeryNumber of ransfused packet cellsNeurological complicationType of nurologic complicationStrokeType of strokeComaMajor wound disruptionInfection of the surgical siteType of bacteria at the surgical siteBacteremiaType of bacteria in bloodOther positive cultures related to surgeryType of culture related to surgeryBacteriaTypeRelatedToSurgeryGraft rejectionUnplanned transfer to intensive care unitDuration in intensive care unit (days)SepsisSeptic ShockSystemic Inflammatory Response Syndrome (SISS)CollectionUnplanned Intubationmv_48h_or_moreunplanned_return_to_orunplanned_or_reasonother_complicationdeath_in_hospital_postophospital_stay_daysanswered_call_followupcomplication_post_dischargeer_visitpost_discharge_complication_typereadmission_related_to_orinfection_or_inflammationredo_surgeryadmission_other_hospitaldeath_post_dischargenotes_description
512735011508900000128.02025-04-0736.0maleBeqaayesgovernment79.0170.027.3overweightNaNsmokingTURBnononononononononononononononono00000nononononononononononokidney transplant (D)Z52.4nononono017.0NaNNaN0.801.26NaN137.0138.0NaN17.514.413.4197.0167.0147.02025-04-0928Transplantationnokidney transplant (D)kidney transplant (D)235.015.0NaN2000.0-NaNNaNGeneralInhalation and IVnono0Fourth floor-Newyesyesyesnononononononono0nonononononononononononononono0nono0nonononononono4yesnonononononononoNaN
513725110518900000120.02025-04-1170.0maleMount Lebanonyesprivate54.0164.020.1normalNaNsmokingJJ removalnonononoyesnonononononononononono00000yesnonononononoyesnononoright inguinal herniaK40yesright inguinal hernianono051.0NaNNaN1.831.13NaN142.0142.0NaN14.2NaNNaN316.0NaNNaN2025-04-1220General surgerynohernia surgeryright inguinal hernia repair56.0NaNNaNNaN-NaNNaNSpinalInhalation and IVnono0TCUnonononononononononono0nonononononononononononononono0nono0nonononononono3noNaNNaNNaNNaNNaNNaNNaNNaNNaN
514751913198900000146.02025-04-2429.0femaleMount Lebanonyesgovernment75.0166.027.2overweightNaNsmokingC/Snononononononononononononononono00000nononononononononononoappendectomyK35.9yesappendicitisyesCTappendicitisNaNNaNNaN0.59NaNNaNNaNNaNNaN13.6NaN12.9317.0NaN300.02025-04-247General surgeryyesappendectomyappendectomy80.05.0NaNNaNno tumorNaNinflammationGeneralInhalation and IVnono0TCUyesnoyesnononononononono0nonononononononononononononono0nono0nonononononono5yesnonononononononoNaN
515734611468900000128.02025-04-0870.0femaleSouth2government84.0152.036.4obeseNaNsmokingexcision of vocal cord polypsnoyesnoyesnononononoyesnononononono00000yesnonononononoyesyesnonocholecystectomyK80nonoyesEchoGB stone-right kidney cyst23.0NaNNaN0.92NaN0.82138.0NaN141.012.5NaN13.7328.0NaN231.02025-04-1428General surgerynocholecystectomycholecystectomy81.013.0NaNNaNno tumorNaNinflammationGeneralInhalation and IVnono0TCUyesnoyesnononononononono0nonononononononononononononono0nono0nonononononono3yesnonononononononoNaN
516736911698900000129.02025-04-083.0femaleSouthnogovernment16.099.016.3underweightNaNno smoking0nononononononononononononononono00000nononononononononononoright distal humerus displaced fractureS42.4yesright distal humerus displaced fractureyesyesright distal humerus displaced fracture14.0NaNNaN0.42NaNNaNNaNNaNNaN11.4NaNNaN352.0NaNNaN2025-04-0929Orthopedic surgerynohumerus surgeryright distal humerus fracture fixation with 3 k wires35.05.0NaNNaN-NaNNaNGeneralInhalation and IVnono0TCUnonononononononononono0nonononononononononononononono0nono0nonononononono1noNaNNaNNaNNaNNaNNaNNaNNaNNaN
517737411748900000130.02025-04-2137.0maleMount Lebanonyesensurance115.0190.031.9obeseNaNsmokingventral hernianoyesnononononononononononononono00000yesyesnononononononononoleft elbow fractureNaNyesleft elbow fracturenono011.0NaNNaN0.83NaNNaN137.0NaNNaN15.1NaNNaN269.0NaNNaN2025-04-2230Orthopedic surgerynolower arm surgeryfixation of proximal ulna fracture and radial head fracture with plates and screws-supinater and LUCL complex sutured and fixed by plate325.0NaNNaNNaN-NaNNaNGeneralInhalation and IVnono0TCUnonononononononononono0nonononononononononononononono0nono0nonononononono3yesnonononononononoNaN
518751613168900000146.02025-04-1745.0maleMount Lebanonyesensurance86.0180.026.5overweightNaNsmokingappendectomymedicationnonononononononononononononono00000nononononononononononofall down-left elbow fractureS52.0yespolytraumayesyesleft olecranon fracture13.0NaNNaN0.93NaNNaN140.0NaNNaN15.2NaNNaN218.0NaNNaN2025-04-1946+29Orthopedic surgerynolower arm surgeryORIF-leftolecranono fracture fixation by 2 kwires65.0NaNNaNNaN-NaNNaNRegionalRegionalInhalation and IVnono0TCUnonoyesnononononononono0nonononononononononononononono0nono0nonononononono2yesnonononononononoNaN
51971519518900000112.02025-04-2229.0malenon lebaneseyesprivate70.0178.022.1normalNaNsmoking0nononononononononononononononono00000nononononononononononoright testicular torsion-right orchiopexyN20yesright testicular torsionyesEchoright testicular torsion13.0NaNNaN0.79NaNNaN139.0NaNNaN16.2NaNNaN276.0NaNNaN2025-04-2212Urologic surgeryyestorsion repairright testicular torsion repair-right orchiopexy10.0NaNNaNNaN-NaNNaNGeneralInhalation and IVnono0TCUyesnoyesnononononononono0nonononononononononononononono0nono0nonononononono2noNaNNaNNaNNaNNaNNaNNaNNaNNaN
520747512758900000141.02025-04-1237.0femalenon lebaneseyesprivateNaNNaNNaNNaNNaNno smoking0nononononononononononononononono00000nononononononononononoaltered LOC-obstructive hydrocephaly-craniopharyngiomaG91.1-D44.4yesICByesCTlesion above the sella turcica and extended superiorly through the 3rd ventricle which obstruct the foramen of mono and causing hydrocephalyNaN17.027.0NaN0.521.54NaN145.0128.0NaN11.910.2NaN388.046.02025-04-1241NeurosurgeryyesEVDEVD insertion in the right ventricle27.0NaNNaNNaN-NaNNaNGeneralInhalation and IVnono0ICUyesnonoyesnonononononono0yeshemorrhagic CVAyeshemorrhagicnonoyesenterococcus faecalisnonoyesCSFacinetobacter baumaniinono0nono0nononoyesEVD torn outnoyes40noNaNNaNNaNNaNNaNNaNNaNNaNThe patient admitted to the hospital with craniopharyngioma causing hydrocephaly. Urgent EVD insertion was done by Dr. Milad Chaalan on 12.4.2025. Wound culture on 21.4.2025: few enterococcus faecalis. EVD was displaced and CSF was leaked all around the EVD insertion site on 22.4.2025. CSF culture on 23.4.2025 and 24.5.2025: heavy acinetobacter baumanii. The patient has torn out the EVD and creation of CSF shunt was done by Dr. Milad Chaalan on 25.4.2025. Intrathecal colistin injection was failed due to obstruction on 26.4.2025. Insertion of EVD drain was done by Dr. Mohamad Yazbeck on 27.4.2025. CSF analysis on 29.4.2025 showed elevated WBCs. Resection of tumor through the carotid optic cisterna pathway was done by Dr. Milad Chaalan on 13.5.2025. CT on 28.4.2025 showed recent 1.2 cm hyperdense hemorrhage at the right hippocampus. Death was on 22.5.2025 due to hemorrhage, meningitis and obstructive hydrocephaly
521738311838900000131.02025-04-2822.0maleMount Lebanonnoprivate93.0173.031.1obeseNaNno smoking0nononononononononononononononono00000nononononononononononoAVM removal-AVM complicated by ICMNaNnononono012.015.018.00.730.740.68140.0141.0140.016.413.815.2182.0172.0227.02025-04-2931Neurosurgerynoexcision of cyst-massleft occipital AVM removal284.020.0NaN600.0no tumorNaNarteriovenous malformationGeneralInhalation and IVnono0ICUyesyesyesyesnonononononono0yesseizurenonononononononononononono0nono0nononononomeningitisno7yesyesyesmeningitisnononononoThe patient readmitted under care of Dr. Mahmoud Younes on 10.5.2025 (6 days post discharge) via ER: seizure, and meningitis.